Search

In this paper, we investigate a very challenging task of automatically generating presentation slides for academic papers. The generated presentation slides can be used as drafts to help the presenters prepare their formal slides in a quicker way. A novel system called PPSGen is proposed to address this task. It first employs the regression method to learn the importance scores of the sentences in an academic paper, and then exploits the integer linear programming (ILP) method to generate well-structured slides by selecting and aligning key phrases and sentences. Evaluation results on a test set of 200 pairs of papers and slides collected on the web demonstrate that our proposed PPSGen system can generate slides with better quality. A user study is also illustrated to show that PPSGen has a few evident advantages over baseline methods. [ABSTRACT FROM PUBLISHER]

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses. For algorithmic modeling, we give a definition of MTL and then classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach and decomposition approach as well as discussing the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, we review online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works in this paper. Finally, we present theoretical analyses and discuss several future directions for MTL. [ABSTRACT FROM AUTHOR]

Event logs that are recorded by information systems provide a valuable starting point for the analysis of processes in various domains, reaching from healthcare, through logistics, to e-commerce. Specifically, behavioral patterns discovered from an event log enable operational insights, even in scenarios where process execution is rather unstructured and shows a large degree of variability. While such behavioral patterns capture frequently recurring episodes of a process’ behavior, they are not limited to sequential behavior but include notions of concurrency and exclusive choices. Existing algorithms to discover behavioral patterns are context-agnostic, though. They neglect the context in which patterns are observed, which severely limits the granularity at which behavioral regularities are identified. In this paper, we therefore present an approach to discover contextual behavioral patterns. Contextual patterns may be frequent solely in a certain partition of the event log, which enables fine-granular insights into the aspects that influence the conduct of a process. Moreover, we show how to analyze the discovered contextual behavioral patterns in terms of causal relations between context information and the patterns, as well as correlations between the patterns themselves. A complete analysis methodology leveraging all the tools presented in the paper and supplemented by interpretations guidelines is also provided. Finally, experiments with real-world event logs demonstrate the effectiveness of our techniques in obtaining fine-granular process insights. [ABSTRACT FROM AUTHOR]

The online retail market shows a sharp increase in traffic during holiday sales. The ability to distinguish customers who will likely purchase is critical for provisioning traffic and for providing cost-effective promotions. This paper uniquely studies the browsing and purchasing behaviors of online shoppers during a yearly sale event in China, the world’s largest online marketplace. Based on 31 million action logs gathered from wide residential areas, we characterize the steps leading to purchases and determine their precursors. We investigate the effect of time (e.g., date, time of date), environment (e.g., platform, viewed category), and action (e.g., session time, clicks, sequence) on purchases. Action cues from shopping behaviors can be used for early detection. While most shoppers start with strong intentions to purchase, yet the moment of ordering comes rather impulsively within 30 seconds to several minutes of browsing. The predictive accuracy reaches as a high AUC of 0.924. The findings in this paper provide an understanding of traffic during mega sale events that can help online shops plan and provide a better user experience for upcoming shopping festivals. [ABSTRACT FROM AUTHOR]

With the prevalence of online social platforms, social recommendation has emerged as a promising direction that leverages the social network among users to enhance recommendation performance. However, the available social relations among users are usually extremely sparse and noisy, which may lead to inferior recommendation performance. To alleviate this problem, this paper novelly exploits the implicit higher-order social influence and dependencies among users to enhance social recommendation. In this paper, we propose a novel embedding method for general bipartite graphs, which defines inter-class message passing between explicit relations and intra-class message passing between implicit higher-order relations via a novel sequential modelling paradigm. Inspired by recent advances in self-attention-based sequential modelling, the proposed model features a self-attentive representation learning mechanism for implicit user-user relations. Moreover, this paper also explores the inductive embedding learning for social recommendation problems to improve the recommendation performance in cold-start settings. The proposed inductive learning paradigm for social recommendation enables embedding inference for those cold-start users and items (unseen during training) as long as they are linked to existing nodes in the original network. Extensive experiments on real-world datasets demonstrate the superiority of our method and suggest that higher-order implicit relationship among users is beneficial to improving social recommendation. [ABSTRACT FROM AUTHOR]

Machine learning research, particularly in genomics, is often based on wide shaped datasets, i.e. datasets having a large number of features, but a small number of samples. Such configurations raise the possibility of chance influence (the increase of measured accuracy due to chance correlations) on the learning process and the evaluation results. Prior research underlined the problem of generalization of models obtained based on such data. In this paper, we investigate the influence of chance on prediction and show its significant effects on wide shaped datasets. First, we empirically demonstrate how significant the influence of chance in such datasets is by showing that prediction models trained on thousands of randomly generated datasets can achieve high accuracy. This is the case even when using cross-validation. We then provide a formal analysis of chance influence and design formal chance influence estimators based on the dataset parameters, namely its sample size, the number of features, the number of classes and the class distribution. Finally, we provide an in-depth discussion of the formal analysis including applications of the findings and recommendations on chance influence mitigation. [ABSTRACT FROM AUTHOR]

While recent developments of neural network (NN) models have led to a series of record-breaking achievements in many applications, the lack of sufficiently good datasets remains a problem for some applications. For such a problem, we can however exploit a large number of unstructured text corpora as an external knowledge to complement the training data, and most prevailing neural network solutions employ word embedding methods for such purposes. In this paper, we propose LDA-Reg, a novel knowledge driven regularization framework based on Latent Dirichlet Allocation (LDA) as an alternative to the word embedding methods to adaptively utilize abundant external knowledge and to interpret the NN model. For the joint learning of the parameters, we propose EM-SGD, an effective update method which incorporates Expectation Maximization (EM) and Stochastic Gradient Descent (SGD) to update parameters iteratively. Moreover, we also devise a lazy update and sparse update method for the high-dimensional inputs and sparse inputs respectively. We validate the effectiveness of our regularization framework through an extensive experimental study over real world and standard benchmark datasets. The results show that our proposed framework not only achieves significant improvement over state-of-the-art word embedding methods but also learns interpretable and significant topics for various tasks. [ABSTRACT FROM AUTHOR]

Competition between Points of Interest (POIs) refers to the situation in which two POIs directly or indirectly provide similar services to secure businesses. A large portion of prior studies on competition analysis focuses on mining textual data, e.g., news articles and social comments. However, the increasing availability of human mobility and mobile query data enables a new paradigm for analyzing the competitive relationships among POIs, which remains largely unexplored. To this end, in this paper, we attempt to mine large-scale online map search query data for better understanding POI competitive relationships. Based on a co-query POI graph built from the map search query data, we develop a novel neural graphlet-based prediction framework to predict the competitive relationships among POIs. A unique perspective of our model is to infer latent POI competitive relationships by integrating multiple distinct factors, e.g., graphlet structure, geographical distance, and regional features, reflected in map search query data and POI data. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness of the proposed framework, and show that our framework outperforms all baselines with a significant margin in all evaluation metrics. [ABSTRACT FROM AUTHOR]

Domain adaptation aims at extracting knowledge from auxiliary source domains to assist the learning task in a target domain. In classification problems, since the distributions of the source and target domains are different, directly using source data to build a classifier for the target domain may hamper the classification performance on the target data. Fortunately, in many tasks, there can be some features that are transferable, i.e., the source and target domains share similar properties. On the other hand, it is common that the source data contain noisy features which may degrade the learning performance in the target domain. This issue, however, is barely studied in existing works. In this paper, we propose to find a feature subset that is transferable across the source and target domains. As a result, the domain discrepancy measured on the selected features can be reduced. Moreover, we seek to find the most discriminative features for classification. To achieve the above goals, we formulate a new sparse learning model that is able to jointly reduce the domain discrepancy and select informative features for classification. We develop two optimization algorithms to address the derived learning problem. Extensive experiments on real-world data sets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the increasing accumulation of spatial-temporal trajectory data, location-based data mining has recently been extensively studied. A fundamental research topic in this field is learning the embedding vectors of locations through self-supervised pre-training. Pre-trained embedding vectors can utilize the highly available unlabeled trajectory data, and benefit downstream tasks in multiple aspects. However, most existing methods ignore the temporal information hidden in the visited time of locations in trajectories. Considering that human activities are highly regulated by specific periods of a day, temporal information can reflect some intrinsic characteristics of locations, so it is necessary to fuse them into location embedding vectors. In this paper, we propose a Time-Aware Location Embedding (TALE) pre-training method based on the CBOW framework, which is able to incorporate temporal information into the learned embedding vectors of locations. A novel temporal tree structure is designed to extract temporal information during the calculation of Hierarchical Softmax. In order to verify the effectiveness of TALE, we apply the learned embedding vectors into three downstream location-based prediction tasks, i.e., location classification, location visitor flow prediction and user next location prediction. Experiments are conducted on four real-world user trajectory datasets, and the experimental results demonstrate that our TALE model can obviously help downstream tasks gain better performance. [ABSTRACT FROM AUTHOR]

Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent transportation systems. Despite years of studies, accurate traffic prediction still faces the following challenges, including modeling the dynamics of traffic data along both temporal and spatial dimensions, and capturing the periodicity and the spatial heterogeneity of traffic data, and the problem is more difficult for long-term forecast. In this paper, we propose an Attention based Spatial-Temporal Graph Neural Network (ASTGNN) for traffic forecasting. Specifically, in the temporal dimension, we design a novel self-attention mechanism that is capable of utilizing the local context, which is specialized for numerical sequence representation transformation. It enables our prediction model to capture the temporal dynamics of traffic data and to enjoy global receptive fields that is beneficial for long-term forecast. In the spatial dimension, we develop a dynamic graph convolution module, employing self-attention to capture the spatial correlations in a dynamic manner. Furthermore, we explicitly model the periodicity and capture the spatial heterogeneity through embedding modules. Experiments on five real-world traffic flow datasets demonstrate that ASTGNN outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

With the boosting of neural networks, recommendation methods become significantly improved by their powerful ability of prediction and inference. Existing neural-network based recommender systems (NN-RSs) usually first employ matrix embedding (ME) as a pre-process to learn users’ and items’ representations (latent vectors), then input these representations to a specific modified neural network framework to make accurate Top-k recommendations. Obviously, the performance of ME has a significant effect on RS models. However, most NN-RSs focus on accuracy by building representations from the direct user-item interactions (e.g., user-item rating matrix), while ignoring the underlying relatedness between users and items (e.g., users who rate the same ratings for the same items should be embedded into similar representations), which is an ideological disadvantage. On the other hand, ME models directly employ inner products as a default loss function metric that cannot project users and items into a proper latent space, which is a methodological disadvantage. In this paper, we propose a supervised collaborative representation learning model - Magnetic Metric Learning (MML) - to map users and items into a unified latent vector space, enhancing the representation learning for NN-RSs. First, MML utilizes dual triplets to model not only the observed relationships between users and items, but also the underlying relationships between users as well as items to overcome the ideological disadvantage. Specifically, a modified metric-based dual loss function is proposed in MML to gather similar entities and disperse the dissimilar ones. With MML, we can easily compare all the relationships (user to user, item to item, user to item) according to the weighted metric, which overcomes the methodological disadvantage. We conduct extensive experiments on four real-world datasets with large item space. The results demonstrate that MML can learn a proper unified latent space for representations from the user-item matrix with high accuracy and effectiveness, and lead to a performance gain over the state-of-the-art RS models by an average of 17 percent. [ABSTRACT FROM AUTHOR]

Semi-supervised learning as one of most attractive problems in machine learning research field has aroused broad attentions in recent years. In this paper, we propose a novel locality preserved dimensionality reduction framework, named Semi-supervised Adaptive Local Embedding learning (SALE), which learns a local discriminative embedding by constructing a $k_1$ k 1 Nearest Neighbors ($k_1$ k 1 NN) graph on labeled data, so as to explore the intrinsic structure, i.e., sub-manifolds from non-Gaussian labeled data. Then, mapping all samples into learned embedding and constructing another $k_2$ k 2 NN graph on all embedded data to explore the global structure of all samples. Therefore, the unlabeled data and their corresponding labeled neighbors can be clustered into same sub-manifold, so as to improve the discriminative power of embedded data. Furthermore, we propose two semi-supervised dimensionality reduction methods with orthogonal and whitening constraints based on proposed SALE framework. An efficient alternatively iterative optimization algorithm is developed to solve the NP-hard problem in our models. Extensive experiments conducted on several synthetic and real-world data sets demonstrate the superiorities of our methods on local structure exploration and classification task. [ABSTRACT FROM AUTHOR]

Numerous state-of-the-art recommendation frameworks employ deep neural networks in Collaborative Filtering (CF). In this paper, we propose a cross feature fusion neural network (CFFNN) for the enhancement of CF. Existing studies overlook either user preferences for various item features or the relationship between item features and user features. To solve this problem, we construct a cross feature fusion network to enable the fusion of user features and item features as well as a self-attention network to determine users’ preferences for items. Specifically, we design a feature extraction layer with multiple MLP (Multilayer Perceptrons) modules to extract both user features and item features. Then, we introduce a cross feature fusion mechanism for an accurate determination of the relationship between different user-item interactions. The features of users and items are crossly embedded and then fed into a prediction network. The attention mechanism enables the model to focus on more effective features. The effectiveness of CFFNN model is demonstrated through extensive experiments on four real-world datasets. The experimental results indicate that CFFNN significantly outperforms the existing state-of-the-art models, with a relative improvement of 3.0 to 12.1 percent on hit ratio (HR) and normalized discounted cumulative gain (NDCG) compared with the baselines. [ABSTRACT FROM AUTHOR]

Mobile social networks include a large number of social members who forward messages cooperatively. However, spammers post links to viruses and advertisements, or follow a large number of users, which produces many misleading messages in mobile social networks. In this paper, we propose an adaptive social spammer detection (ASSD) model. We build a spammer classifier by using a small number of labeled patterns and some unlabeled patterns. The prediction accuracy is high compared with some conventional supervised learning methods. Moreover, the time and energy required to label the identity of social members are reduced by applying ASSD. Because social spammers frequently change their behavior to deceive the spammer detection model, an incremental learning method is designed to update the spammer detection model adaptively, without retraining. We evaluate ASSD by comparing it with other supervised and semi-supervised machine learning methods using the Social Honeypot Dataset. Experimental results show that the proposed model outperforms the baseline methods in terms of recall and precision. Additionally, ASSD maintains a high detection accuracy by adaptively updating the model with newly generated social media data. [ABSTRACT FROM AUTHOR]

Social recommendation has emerged to leverage social connections among users for predicting users’ unknown preferences, which could alleviate the data sparsity issue in collaborative filtering based recommendation. Early approaches relied on utilizing each user’s first-order social neighbors’ interests for better user modeling, and failed to model the social influence diffusion process from the global social network structure. Recently, we propose a preliminary work of a neural influence Diffusion Network (i.e., DiffNet) for social recommendation L. Wu, P. Sun, Y. Fu, R. Hong, X. Wang, and M. Wang, “A neural influence diffusion model for social recommendation,” in Proc. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2019, pp. 235–244.. DiffNet models the recursive social diffusion process for each user, such that the influence diffusion hidden in the higher-order social network is captured in the user embedding process. Despite the superior performance of DiffNet, we argue that, as users play a central role in both user-user social network and user-item interest network, only modeling the influence diffusion process in the social network would neglect the latent collaborative interests of users hidden in the user-item interest network. To this end, in this paper, we propose DiffNet++, an improved algorithm of DiffNet that models the neural influence diffusion and interest diffusion in a unified framework. By reformulating the social recommendation as a heterogeneous graph with social network and interest network as input, DiffNet++ advances DiffNet by injecting both the higher-order user latent interest reflected in the user-item graph and higher-order user influence reflected in the user-user graph for user embedding learning. This is achieved by iteratively aggregating each user’s embedding from three aspects: the user’s previous embedding, the influence aggregation of social neighbors from the social network, and the interest aggregation of item neighbors from the user-item interest network. Furthermore, we design a multi-level attention network that learns how to attentively aggregate user embeddings from these three aspects. Finally, extensive experimental results on four real-world datasets clearly show the effectiveness of our proposed model. We release the source code at https://github.com/PeiJieSun/diffnet. [ABSTRACT FROM AUTHOR]

Understanding intra-urban human mobility patterns and their potential driving forces are vital to city planning and commercial site selection. In this paper, we first investigate the functions of urban regions and how different region types dynamically influence people’s trip decisions. Furthermore, we characterize urban circadian rhythms by time-vary inter-regional transition probabilities between these regions with different functions, and integrate them into intervening opportunity model to predict human mobility. Public transportation card data in Shanghai are used to demonstrate the effectiveness of the model in terms of station passenger flows, travel time and trip flux. By taking regional function into consideration, the proposed model significantly improved the prediction accuracy. Quantitative analysis ulteriorly indicates that trip intentions and regional features are critical elements in trip flux prediction, especially in the afternoon and evening when people have an abundance of opportunities to travel by their own volition. When the function of a certain region changes, our model is able to make reasonable predictions accordingly. The results indicate the importance of considering individual travel motivation and regional function in modeling human mobility. The proposed model could serve as a guide for popularity and trip flux prediction in urban planning and reconstruction. [ABSTRACT FROM AUTHOR]

Active learning is an effective approach for tasks with limited labeled data. It samples a small set of data to annotate actively and is widely applied in various AI tasks. It uses an iterative process, during which we utilize the current trained model to evaluate all unlabeled samples and annotate the best samples based on a specific query strategy to update the underlying model iteratively. Most existing active learning approaches rely on only the evaluation results generated by the current model and ignore the results from previous iterations. In this paper, we propose using more historical evaluation results which can provide additional information to help better select samples. First, we apply two kinds of heuristic features of the historical evaluation results, the weighted sum of historical results and the fluctuation of the historical evaluation sequence, to improve the effectiveness of active learning sampling. Next, to further and more globally use the information contained in the historical results, we design a novel query strategy that learns how to select samples based on the historical sequences automatically. Our proposed idea is general and can be combined with both basic and state-of-the-art query strategies to achieve improvements. We test our approaches on two common NLP tasks including text classification and named entity recognition. Experimental results show that our methods significantly promote existing methods. [ABSTRACT FROM AUTHOR]

Community search that finds query-dependent communities has been studied on various kinds of graphs. As one instance of community search, intimate-core group (community) search over a weighted graph is to find a connected $k$ k -core containing all query nodes with the smallest group weight. However, existing state-of-the-art methods start from the maximal $k$ k -core to refine an answer, which is practically inefficient for large networks. In this paper, we develop an efficient framework, called local exploration k-core search (LEKS), to find intimate-core groups in graphs. We propose a small-weighted spanning tree to connect query nodes, and then expand the tree level by level to a connected $k$ k -core, which is finally refined as an intimate-core group. In addition, to support the intimate group search over large weighted graphs, we develop a weighted-core index (WC-index) and two new WC-index-based algorithms for expansion and refinement phases in LEKS. Specifically, we propose a WC-index-based expansion to efficiently find a candidate graph of intimate-core group, leveraging on a two-level expansion of $k$ k -breadth and 1-depth. We propose two graph removal strategies: coarse-grained refinement is designed for large graphs to delete a batch of nodes in a few iterations; fine-grained refinement is designed for small graphs to remove nodes carefully and achieve high-quality answers. Extensive experiments on real-life networks with ground-truth communities validate the effectiveness and efficiency of our proposed methods. [ABSTRACT FROM AUTHOR]

Knowledge graphs (KGs) have become popular structures for unifying real-world entities by modelling the relationships between them and their attributes. To support multilingual applications, a significant number of language-specific KGs have been built by different parties using various data sources. As a result, these monolingual KGs are often disconnected, causing semantic heterogeneity and detracting from the original purpose of KGs. Entity alignment – the task of identifying corresponding entities across different KGs – has attracted a great deal of attention in both academia and industry. However, existing alignment techniques often require large amounts of labelled data, are unable to encode multi-modal data simultaneously, and enforce only a few consistency constraints. In this paper, we propose an end-to-end, unsupervised entity alignment framework for cross-lingual KGs that fuses different types of information in order to fully exploit the richness of KG data. The model captures the relation-based correlation between entities by using a multi-order graph convolutional neural (GCN) model that is designed to satisfy the consistency constraints, while incorporating the attribute-based correlation via a translation machine. We adopt a late-fusion mechanism to combine all the information together, which allows these approaches to complement each other and thus enhances the final alignment result, and makes the model more robust to consistency violations. Empirical results for various scenarios on real-world and synthetic KGs show that our model is up to 22.71 percent more accurate and orders of magnitude faster than existing baselines. We also demonstrate its sensitivity to hyper-parameters, effort saving in terms of labelling, and the robustness against adversarial conditions. [ABSTRACT FROM AUTHOR]

Wide models such as generalized linear models and factorization-based models have been extensively used in various predictive applications, e.g., recommendation, CTR prediction, and image recognition. Due to the memory bounded property of the models, the performance improvement on CPU is reaching the limitation. GPU is known to have many computation units and high memory bandwidth, and becomes a promising platform for training machine learning models. However, the GPU training for the wide models is far from optimal due to the sparsity and irregularity in wide models. The existing GPU-based wide models are even slower than the ones using CPU. The classical training schema of the wide models does not optimized for the GPU architecture, which suffers from large amount of random memory accesses and redundant read/write of intermediate values. In this paper, we propose an efficient GPU-training framework for the large-scale wide models, named cuWide. To fully benefit from the memory hierarchy of GPU, cuWide applies a new flow-based schema for training, which leverages the spatial and temporal locality of wide models to drastically reduce the amount of communication with GPU global memory. To do so, we adopt a bigraph computation model to efficiently realize the flow-based schema and exploit three flexible interfaces for programming. Further, we use the 2D partition of mini-batch (in sample and feature dimensions) with proposed graph abstraction to optimize GPU memory access for sparse data, and apply several spatial-temporal caching mechanisms (importance-based model caching and cross-stage accumulation caching mechanisms) to achieve a high performance kernel. To efficiently implement cuWide, we also propose several GPU-oriented optimizations, including feature-oriented data layout to enhance the data locality, replication mechanism to reduce update conflicts in shared memory, and multi-stream scheduling to overlap data transferring and kernel computing. We show that cuWide can be up to more than 20× faster than the state-of-the-art GPU solutions and multi-core CPU solutions. [ABSTRACT FROM AUTHOR]

The growing popularity of dynamic applications such as social networks provides a promising way to detect valuable information in real time. These applications create high-speed data that can be easily modeled as streaming graph. Efficient analysis over these data is of great significance. In this paper, we study the subgraph (isomorphism) search over streaming graph data that obeys timing order constraints over the occurrence of edges in the stream. The sliding window model is employed to focus on the most recent data. We propose an efficient solution to answer subgraph search, introduce optimizations to greatly reduce the space cost, and design concurrency management to improve system throughput. Extensive experiments on real network traffic data and synthetic social streaming data shows that our solution outperforms comparative ones by one order of magnitude with less space cost. [ABSTRACT FROM AUTHOR]

With the popularity of location-aware devices (e.g., smart phones), large amounts of location-based social media data such as check-ins are generated. This stimulates plenty of studies for POI recommendations by applying machine learning techniques. However, most of the existing studies focus on POI recommendations in the same city or region, and fail to recommend POIs for users when they travel to a new city. In this paper, we propose a novel deep neural network, named as ST-TransRec, for crossing-city POI recommendations. It integrates the deep neural network, transfer learning technique, and density-based resampling method into a unified framework. In this model, the deep neural network is used to capture users’ preferences for POIs and learn the embeddings of POIs. Besides, the transfer learning technique is employed to bridge the gap between cities that results from the city-dependent features. As the distributions over POIs are imbalanced, we design a density-based spatial resampling model which enables POIs to be well matched across cities. We conduct extensive experiments on two real-world datasets. The experimental results show the advantages of ST-TransRec over the state-of-the-art methods for crossing-city POI recommendations. [ABSTRACT FROM AUTHOR]

With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temporal data is critically important to many real-world applications including human mobility understanding, smart transportation, urban planning, public safety, health care and environmental management. As the number, volume and resolution of spatio-temporal data increase rapidly, traditional data mining methods, especially statistics-based methods for dealing with such data are becoming overwhelmed. Recently deep learning models such as recurrent neural network (RNN) and convolutional neural network (CNN) have achieved remarkable success in many domains due to the powerful ability in automatic feature representation learning, and are also widely applied in various spatio-temporal data mining (STDM) tasks such as predictive learning, anomaly detection and classification. In this paper, we provide a comprehensive review of recent progress in applying deep learning techniques for STDM. We first categorize the spatio-temporal data into five different types, and then briefly introduce the deep learning models that are widely used in STDM. Next, we classify existing literature based on the types of spatio-temporal data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains including transportation, on-demand service, climate & weather analysis, human mobility, location-based social network, crime analysis, and neuroscience. Finally, we conclude the limitations of current research and point out future research directions. [ABSTRACT FROM AUTHOR]

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms and the state-of-the-art pretrained language model, i.e., BERT, on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results. [ABSTRACT FROM AUTHOR]

Personalized venues recommendation is essential to help people find attractive venue to visit as growth of location-based social networks. Existing approaches never distinguish user individual interests from her social preferences, which leads to a bottleneck of modeling user check-in behaviors accurately. In this paper, we find the differences between user interests and her social preferences clearly and investigate the time law of user check-in behaviors in depth. Consequently, we propose a social-period-aware topic model (SPATM) to learn the influence weights of both user interests and her social preferences on making-decision for each check-in time automatically. Especially, we model latent topic by leveraging smaller size of dynamic activities instead of static categories, which can alleviate the data sparsity problem by using more co-occurrent activities information. Moreover, our approach can automatically judge whether a user’s social preference is periodic or aperiodic and learn the periodicity of periodic one. Furthermore, the Alias Sampling based training approach is introduced to improve sampling efficiency. The results demonstrate our proposed model is effective and outperforms the state-of-the-art approaches in terms of effectiveness and efficiency. Besides, SPATM can learn semantically coherent latent topics and geographically dispersed latent social topics which are useful to explain recommendation. [ABSTRACT FROM AUTHOR]

Over the years, quantifying the similarity of nodes has been a hot topic in network science, yet little has been known about the distribution of node-similarity. In this paper, we consider a typical measure of node-similarity called the common neighbor based similarity (CNS). By means of the generating function, we propose a general framework for calculating the CNS distributions of node sets in various networks. Particularly, we show that for the Erdös-Rényi random network, the CNS distribution of node sets of any size obeys the Poisson law. Furthermore, we connect the node-similarity distribution to the link prediction problem, and derive analytical solutions for two key evaluation metrics: i) precision and ii) area under the receiver operating characteristic curve (AUC). We also use the similarity distributions to optimize link prediction by i) deriving the expected prediction accuracy of similarity scores and ii) providing the optimal prediction priority of unconnected node pairs. Simulation results confirm our theoretical findings and also validate the proposed tools in evaluating and optimizing link prediction. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel Semi-supervised Learning with Incomplete Modality (SLIM) method considering the modal consistency and complementarity simultaneously, and Kernel SLIM (SLIM-K) based on matrix completion for further solving the modal incompleteness. As is well known, most realistic data have multi-modal representations, multi-modal learning refers to the process of learning a precise model for complete modalities. However, due to the failures of data collection, self-deficiencies, or other various reasons, multi-modal examples are usually with incomplete modalities, which generate utility obstacle using previous methods. In this paper, SLIM integrates the intrinsic consistency and extrinsic complementary information for prediction and cluster simultaneously. In detail, SLIM forms different modal classifiers and clustering learner consistently in a unified framework, while using the extrinsic complementary information from unlabeled data against the insufficiencies brought by the incomplete modal issue. Moreover, in order to deal with missing modality in essence, we propose the SLIM-K, which takes the complemented kernel matrix into the classifiers and the cluster learner respectively. Thus, SLIM-K can solve the defects of missing modality in result. Finally, we give the discussion of generalization of incomplete modalities. Experiments on 13 benchmark multi-modal datasets and two real-world incomplete multi-modal datasets validate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

Network embedding has been widely employed in networked data mining applications as it can learn low-dimensional and dense node representations from the high-dimensional and sparse network structure. While most existing network embedding methods only model the proximity between two nodes regardless of the order of the proximity, this paper proposes to explicitly model multi-node proximities which can be widely observed in practice, e.g., multiple researchers coauthor a paper, and multiple genes co-express a protein. Explicitly modeling multi-node proximities is important because some two-node interactions may not come into existence without a third node. By proving that LINE(1st), a recent network embedding method, is equivalent to kernelized matrix factorization, this paper proposes coupled kernelized multi-dimensional array factorization (Cetera) which jointly factorizes multiple multi-dimensional arrays by enforcing a consensus representation for each node. In this way, node representations can be more comprehensive and effective, which is demonstrated on three real-world networks through link prediction and multi-label classification. [ABSTRACT FROM AUTHOR]

In crowdsourcing, human workers are employed to tackle problems that are traditionally difficult for computers (e.g., data cleaning, missing value filling, and sentiment analysis). In this paper, we study the effective use of crowdsourcing in filling missing values in a given relation (e.g., a table containing different attributes of celebrity stars, such as nationality and age). A task given to a worker typically consists of questions about the missing attribute values (e.g., What is the age of Jet Li?). Although this problem has been studied before, existing work often treats related attributes independently, leading to suboptimal performance. In this paper, we present T-Crowd, which is a crowdsourcing system that considers attribute relationships. Particularly, T-Crowd integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values. The attribute relationship information is used to guide task allocation to workers. Our solution seamlessly supports categorical and continuous attributes. Our extensive experiments on real and synthetic datasets show that T-Crowd outperforms state-of-the-art methods, improving the quality of truth inference and reducing the monetary cost of crowdsourcing. [ABSTRACT FROM AUTHOR]

In this paper, we propose two GAN-based deep subspace clustering approaches: deep subspace clustering via dual adversarial generative networks (DSC-DAG) and self-supervised deep subspace clustering with adversarial generative networks (S $^2$ 2 DSC-AG). In DSC-DAG, the distributions of both the inputs and corresponding latent representations are learnt via adversarial training simultaneously. Besides, there are two kinds of synthetic representations to facilitate the fine-tuning of the encoder: the combinations of latent representations with random combination coefficients and representations of real-like inputs derived from noise variables. In S $^2$ 2 DSC-AG, a self-supervised information learning module substitutes for adversarial learning in the latent space, since both of them play the same role in learning discriminative latent representations. We analyze connections between these methods and demonstrate their equivalences. We conduct extensive experiments on multiple real-world data sets against state-of-the-art subspace clustering methods in terms of accuracy, normalized mutual information and purity. Experimental results demonstrate the effectiveness and superiority of our proposed methods. [ABSTRACT FROM AUTHOR]

It has been an important task for recommender systems to suggest satisfying activities to a group of users in people’s daily social life. The major challenge in this task is how to aggregate personal preferences of group members to infer the decision of a group. Conventional group recommendation methods applied a predefined strategy for preference aggregation. However, these static strategies are too simple to model the real and complex process of group decision-making, especially for occasional groups which are formed ad-hoc. Moreover, group members should have non-uniform influences or weights in a group, and the weight of a user can be varied in different groups. Therefore, an ideal group recommender system should be able to accurately learn not only users’ personal preferences but also the preference aggregation strategy from data. In this paper, we propose a novel end-to-end group recommender system named CAGR (short for “Centrality-Aware Group Recommender”), which takes Bipartite Graph Embedding Model (BGEM), the self-attention mechanism and Graph Convolutional Networks (GCNs) as basic building blocks to learn group and user representations in a unified way. Specifically, we first extend BGEM to model group-item interactions, and then in order to overcome the limitation and sparsity of the interaction data generated by occasional groups, we propose a self-attentive mechanism to represent groups based on the group members. In addition, to overcome the sparsity issue of user-item interaction data, we leverage the user social networks to enhance user representation learning, obtaining centrality-aware user representations. To further alleviate the group data sparsity problem, we propose two model optimization approaches to seamlessly integrate the user representations learning process. We create three large-scale benchmark datasets and conduct extensive experiments on them. The experimental results show the superiority of our proposed CAGR by comparing it with state-of-the-art group recommender models. [ABSTRACT FROM AUTHOR]

Social recommendation, which utilizes social relations to enhance recommender systems, has been gaining increasing attention recently with the rapid development of online social networks. Existing social recommendation methods are based on the assumption, so-called social-trust, that users’ preference or decision is influenced by their social-connected friends’ purchase behaviors. However, they assume that the influences of social relationships are always the same, which violates the fact that users are likely to share preference on different products with different friends. More precisely, friends’ behaviors do not necessarily affect a user’s preferences, and the influence is diverse among different items. In this paper, we contribute a new solution, CSR (short for Characterized Social Regularization) model by designing a universal regularization term for modeling variable social influence. This regularization term captures the finely grained similarity of social-connected friends. We further introduce two variants of our model with different optimization manners. Our proposed model can be applied to both explicit and implicit interaction due to its high generality. Extensive experiments on three real-world datasets demonstrate that our CSR can outperform state-of-the-art social recommendation methods. Further experiments show that CSR can improve recommendation performance for those users with sparse social relations or behavioral interactions. [ABSTRACT FROM AUTHOR]

Representation learning has been proven to play an important role in the unprecedented success of machine learning models in numerous tasks, such as machine translation, face recognition and recommendation. The majority of existing representation learning approaches often require a large number of consistent and noise-free labels. However, due to various reasons such as budget constraints and privacy concerns, labels are very limited in many real-world scenarios. Directly applying standard representation learning approaches on small labeled data sets will easily run into over-fitting problems and lead to sub-optimal solutions. Even worse, in some domains such as education, the limited labels are usually annotated by multiple workers with diverse expertise, which yields noises and inconsistency in such crowdsourcing settings. In this paper, we propose a novel framework which aims to learn effective representations from limited data with crowdsourced labels. Specifically, we design a grouping based deep neural network to learn embeddings from a limited number of training samples and present a Bayesian confidence estimator to capture the inconsistency among crowdsourced labels. Furthermore, to expedite the training process, we develop a hard example selection procedure to adaptively pick up training examples that are misclassified by the model. Extensive experiments conducted on three real-world data sets demonstrate the superiority of our framework on learning representations from limited data with crowdsourced labels, comparing with various state-of-the-art baselines. In addition, we provide a comprehensive analysis on each of the main components of our proposed framework and also introduce the promising results it achieved in our real production to fully understand the proposed framework. To encourage reproducible results, we make our code available online at https://github.com/tal-ai/RECLE. [ABSTRACT FROM AUTHOR]

Unsupervised spectral feature selection (USFS) methods could output interpretable and discriminative results by embedding a Laplacian regularizer in the framework of sparse feature selection to keep the local similarity of the training samples. To do this, USFS methods usually construct the Laplacian matrix using either a general-graph or a hyper-graph on the original data. Usually, a general-graph could measure the relationship between two samples while a hyper-graph could measure the relationship among no less than two samples. Obviously, the general-graph is a special case of the hyper-graph and the hyper-graph may capture more complex structure of samples than the general graph. However, in previous USFS methods, the construction of the Laplacian matrix is separated from the process of feature selection. Moreover, the original data usually contain noise. Each of them makes difficult to output reliable feature selection models. In this paper, we propose a novel feature selection method by dynamically constructing a hyper-graph based Laplacian matrix in the framework of sparse feature selection. Experimental results on real datasets showed that our proposed method outperformed the state-of-the-art methods in terms of both clustering and segmentation tasks. [ABSTRACT FROM AUTHOR]

One of the main challenges in multi-source domain adaptation is how to reduce the domain discrepancy between each source domain and a target domain, and then evaluate the domain relevance to determine how much knowledge should be transferred from different source domains to the target domain. However, most prior approaches barely consider both discrepancies and relevance among domains. In this paper, we propose an algorithm, called Iterative Refinement based on Feature Selection and the Wasserstein distance (IRFSW), to solve semi-supervised domain adaptation with multiple sources. Specifically, IRFSW aims to explore both the discrepancies and relevance among domains in an iterative learning procedure, which gradually refines the learning performance until the algorithm stops. In each iteration, for each source domain and the target domain, we develop a sparse model to select features in which the domain discrepancy and training loss are reduced simultaneously. Then a classifier is constructed with the selected features of the source and labeled target data. After that, we exploit optimal transport over the selected features to calculate the transferred weights. The weight values are taken as the ensemble weights to combine the learned classifiers to control the amount of knowledge transferred from source domains to the target domain. Experimental results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

How to accurately predict unknown quality-of-service (QoS) data based on observed ones is a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) model has shown its efficiency in addressing this issue owing to its high accuracy and scalability. An LF model can be improved by identifying user and service neighborhoods based on user and service geographical information. However, such information can be difficult to acquire in most applications with the considerations of information security, identity privacy, and commercial interests in a real system. Besides, the existing LF model-based QoS predictors mostly ignore the reliability of given QoS data where noises commonly exist to cause accuracy loss. To address the above issues, this paper proposes a data-characteristic-aware latent factor (DCALF) model to implement highly accurate QoS predictions, where ‘data-characteristic-aware’ indicates that it can appropriately implement QoS prediction according to the characteristics of given QoS data. Its main idea is two-fold: a) it detects the neighborhoods and noises of users and services based on the dense LFs extracted from the original sparse QoS data, b) it incorporates a density peaks-based clustering method into its modeling process for achieving the simultaneous detections of both neighborhoods and noises of QoS data. With such designs, it precisely represents the given QoS data in spite of their sparsity, thereby achieving highly accurate predictions for unknown ones. Experimental results on two QoS datasets generated by real-world Web services demonstrate that the proposed DCALF model outperforms state-of-the-art QoS predictors, making it highly competitive in addressing the issue of Web service selection and recommendation. [ABSTRACT FROM AUTHOR]

The excavated information will become obsolete when the data changes in dynamic graphs. To compute the up-to-date results, the graph algorithm has to re-compute the entire data from scratch, which will consume huge computation time and resources. To reduce the cost of such calculations, this paper proposes a model called IncGraph to support incremental iterative computation over dynamic graphs. Different from the way of traditional iteration, IncGraph executes the graph algorithm through reusing the results of the previous graph and performs computation on the part of the graph that has changed. IncGraph has two critical components: (1) an incremental iterative computation model that consists of two steps: an incremental step to calculate the results on the changed vertices of the graph, and a merge step to calculate the results on the entire graph by using the results of the previous graph and the incremental step; and (2) an incremental update method to accelerate the iterative process within the iterative graph algorithm. We implement IncGraph model on GraphX and evaluate its performance by using several representative iterative graph algorithms: PageRank, Connected components, and Single Source Shortest Path. The results show that compared with the traditional iteration, when adding the 100k of vertices in different size data sets, the performance optimization ratio of IncGraph is 31.79 percent averagely, and 50.2 percent maximum; and when the percentage of added vertices varied from 0.01 to 10 percent in different data sets, the performance optimization ratio of IncGraph varied from 19.9 to 66.1 percent. Moreover, the result errors of IncGraph is small and can be neglected. [ABSTRACT FROM AUTHOR]

Over the years, model-based approaches have shown their effectiveness in computing recommendation lists in different domains and settings. By relying on the computation of latent factors, they can recommend items with a very high level of accuracy. Unfortunately, when moving to the latent space, even if the model embeds content-based information, we miss references to the actual semantics of the recommended item. It makes the interpretation of the recommendation process non-trivial. In this paper, we show how to initialize latent factors in Factorization Machines by using semantic features coming from knowledge graphs to train an interpretable model, which is, in turn, able to provide recommendations with a high level of accuracy. In the presented approach, semantic features are injected into the learning process to retain the original informativeness of the items available in the dataset. By relying on the information encoded in the original knowledge graph, we also propose two metrics to evaluate the semantic accuracy and robustness of knowledge-aware interpretability. An extensive experimental evaluation on six different datasets shows the effectiveness of the interpretable model in terms of both accuracy and diversity of recommendation results and interpretability robustness. [ABSTRACT FROM AUTHOR]

Next Point-of-Interest (POI) recommendation which is of great value to both users and POI holders is a challenging task since complex sequential patterns and rich contexts are contained in extremely sparse user check-in data. Recently proposed embedding techniques have shown promising results in alleviating the data sparsity issue by modeling context information, and Recurrent Neural Network (RNN) has been proved effective in the sequential prediction. However, existing next POI recommendation approaches train the embedding and network model separately, which cannot fully leverage rich contexts. In this paper, we propose a novel unified neural network framework, named NeuNext, which leverages POI context prediction to assist next POI recommendation by joint learning. Specifically, the Spatio-Temporal Gated Network (STGN) is proposed to model personalized sequential patterns for users’ long and short term preferences in the next POI recommendation. In the POI context prediction, rich contexts on POI sides are used to construct graph, and enforce the smoothness among neighboring POIs. Finally, we jointly train the POI context prediction and the next POI recommendation to fully leverage labeled and unlabeled data. Extensive experiments on real-world datasets show that our method outperforms other approaches for next POI recommendation in terms of Accuracy and MAP. [ABSTRACT FROM AUTHOR]

Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding, and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift. [ABSTRACT FROM AUTHOR]

Large-scale optimization has become an important application for data management systems, particularly in the context of statistical machine learning. In this paper, we consider how one might implement the join-and-co-group pattern in the context of a fully declarative data processing system. The join-and-co-group pattern is ubiquitous in iterative, large-scale optimization. In the join-and-co-group pattern, a user-defined function $g$g is parameterized with a data object $x$x as well as the subset of the statistical model $\Theta _x$Θx that applies to that object, so that $g(x | \Theta _x)$g(x|Θx) can be used to compute a partial update of the model. This is repeated for every $x$x in the full data set $X$X. All partial updates are then aggregated and used to perform a complete update of the model. The join-and-co-group pattern has several implementation challenges, including the potential for a massive blow-up in the size of a fully parameterized model. Thus, unless the correct physical execution plan be chosen for implementing the join-and-co-group pattern, it is easily possible to have an execution that takes a very long time or even fails to complete. In this paper, we carefully consider the alternatives for implementing the join-and-co-group pattern on top of a declarative system, as well as how the best alternative can be selected automatically. Our focus is on the SimSQL database system, which is an SQL-based system with special facilities for large-scale, iterative optimization. Since it is an SQL-based system with a query optimizer, those choices can be made automatically. [ABSTRACT FROM AUTHOR]

Usually considered as a classification problem, entity resolution (ER) can be very challenging on real data due to the prevalence of dirty values. The state-of-the-art solutions for ER were built on a variety of learning models (most notably deep neural networks), which require lots of accurately labeled training data. Unfortunately, high-quality labeled data usually require expensive manual work, and are therefore not readily available in many real scenarios. In this paper, we propose a novel learning paradigm for ER, called gradual machine learning, which aims to enable effective machine labeling without the requirement for manual labeling effort. It begins with some easy instances in a task, which can be automatically labeled by the machine with high accuracy, and then gradually labels more challenging instances by iterative factor graph inference. In gradual machine learning, the hard instances in a task are gradually labeled in small stages based on the estimated evidential certainty provided by the labeled easier instances. Our extensive experiments on real data have shown that the performance of the proposed approach is considerably better than its unsupervised alternatives, and highly competitive compared to the state-of-the-art supervised techniques. Using ER as a test case, we demonstrate that gradual machine learning is a promising paradigm potentially applicable to other challenging classification tasks requiring extensive labeling effort. [ABSTRACT FROM AUTHOR]

Spatial structured models are predictive models that capture dependency structure between samples based on their locations in the space. Learning such models plays an important role in many geoscience applications such as water surface mapping, but it also poses significant challenges due to implicit dependency structure in continuous space and high computational costs. Existing models often assume that the dependency structure is based on either spatial proximity or network topology, and thus cannot incorporate complex dependency structure such as contour and flow direction on a 3D potential surface. To fill the gap, we recently proposed a novel spatial structured model called hidden Markov contour tree (HMCT), which generalizes the traditional hidden Markov model from a total order sequence to a partial order polytree. HMCT also advances existing work on hidden Markov trees through capturing complex contour structures on a 3D surface. We proposed efficient model construction and learning algorithms. This paper extends our initial HMCT model into a post-processor that can refine the classified results from other existing models. We analyzed the theoretical properties of the extended model. Evaluations on real-world flood mapping datasets show that HMCT outperforms multiple baseline methods in classification performance and the HMCT can also effectively enhance the results of other baseline methods. Computational experiments also show that HMCT is scalable to large data sizes (e.g., classifying millions of samples in seconds). [ABSTRACT FROM AUTHOR]

A growing specter in the rise of machine learning is whether the decisions made by machine learning models are fair. While research is already underway to formalize a machine-learning concept of fairness and to design frameworks for building fair models with sacrifice in accuracy, most are geared toward either supervised or unsupervised learning. Yet two observations inspired us to wonder whether semi-supervised learning might be useful to solve discrimination problems. First, previous study showed that increasing the size of the training set may lead to a better trade-off between fairness and accuracy. Second, the most powerful models today require an enormous of data to train which, in practical terms, is likely possible from a combination of labeled and unlabeled data. Hence, in this paper, we present a framework of fair semi-supervised learning in the pre-processing phase, including pseudo labeling to predict labels for unlabeled data, a re-sampling method to obtain multiple fair datasets and lastly, ensemble learning to improve accuracy and decrease discrimination. A theoretical decomposition analysis of bias, variance and noise highlights the different sources of discrimination and the impact they have on fairness in semi-supervised learning. A set of experiments on real-world and synthetic datasets show that our method is able to use unlabeled data to achieve a better trade-off between accuracy and discrimination. [ABSTRACT FROM AUTHOR]

The widespread use of positioning devices has given rise to many trajectories, with each having three explicit attributes: user ID, location ID, and time-stamp and an implicit attribute: activity type (akin to “topic” in text mining). To model these trajectories, existing works learn different attribute representations by either introducing latent activity types based on topic models or transforming the location and time context into a low-dimensional space via embedding techniques. In this paper, we propose a holistic approach named Human Mobility Representation Model (HMRM) to simultaneously produce the vector representations of all four (explicit and implicit) attributes. The merits of HMRM lie in that: (1) it models the latent activity types and learns trajectory attribute embeddings in an integrated manner, and (2) it connects the activity-related distributions and these attributes embeddings by adding a newly designed collaborative learning component, and makes them mutually exchanged to take the best of both worlds. We apply HMRM to both unsupervised and supervised tasks including two activity evaluation tasks and two embedding evaluation tasks, on two real check-in datasets collected from Foursquare. Experimental results show that HMRM could not only improve the performance of capturing latent activity types, but also learn better trajectory embeddings. [ABSTRACT FROM AUTHOR]

The society’s insatiable appetites for personal data are driving the emergence of data markets, allowing data consumers to launch customized queries over the datasets collected by a data broker from data owners. In this paper, we study how the data broker can maximize its cumulative revenue by posting reasonable prices for sequential queries. We thus propose a contextual dynamic pricing mechanism with the reserve price constraint, which features the properties of ellipsoid for efficient online optimization and can support linear and non-linear market value models with uncertainty. In particular, under low uncertainty, the proposed pricing mechanism attains a worst-case cumulative regret logarithmic in the number of queries. We further extend our approach to support other similar application scenarios, including hospitality service and online advertising, and extensively evaluate all three use cases over MovieLens 20M dataset, Airbnb listings in U.S. major cities, and Avazu mobile ad click dataset, respectively. The analysis and evaluation results reveal that: (1) our pricing mechanism incurs low practical regret, while the latency and memory overhead incurred is low enough for online applications; and (2) the existence of reserve price can mitigate the cold-start problem in a posted price mechanism, thereby reducing the cumulative regret. [ABSTRACT FROM AUTHOR]

User identification across multiple datasets has a wide range of applications and there has been an increasing set of research works on this topic during recent years. However, most of existing works focus on user identification with a single input data type, e.g., (I) identifying a user across multiple social networks with online data and (II) detecting a single user from heterogeneous trajectory datasets with offline data. Different from previous works, in this paper, we propose a framework on user identification between online and offline datasets. We build connections between these two types of data by a mapping from IP addresses to physical locations. To solve this problem, we propose a novel framework consisting of three steps. First, we use a clustering method based on locations of IP addresses to map IP addresses into specific physical location distributions. Second, we propose a novel pairwise index to reduce space cost and running time for computing the co-occurrence. Lastly, we apply a learning-to-rank method to merge the effect of multiple features we get in the first two steps. Based on our framework, we design experiments to demonstrate the efficiency (in time and space) of our framework, together with the precision and recall of our approach compared to other methods. [ABSTRACT FROM AUTHOR]

Several systems proposed for processing keyword queries over relational databases rely on the generation and evaluation of Candidate Networks (CNs), i.e., networks of joined database relations that when processed as SQL queries, provide a relevant answer to the input keyword query. Although the evaluation of CNs has been extensively addressed in the literature, the problem of generating CNs efficiently and effectively has received much less attention. This challenging problem consists of automatically locating relations in the database that may contain relevant pieces of information, given a handful of keywords, and determining suitable ways of joining these relations to satisfy the implicit information needs expressed by a user while formulating his/her query. In this paper, we propose a novel approach for generating CNs, wherein the possible matches for the query in the database are efficiently enumerated at first. These query matches are then used to guide the CN generation process, avoiding the exhaustive search procedure used by the current state-of-art approaches. We show that our approach allows the generation of a compact set of CNs that leads to superior quality answers, and demands less resources in terms of processing time and memory. These claims are supported by a comprehensive set of experiments that we carried out using several query sets and datasets used in previous related works and whose results we report and analyze here. [ABSTRACT FROM AUTHOR]

Deep learning is increasingly popular, partly due to its widespread application potential, such as in civilian, government and military domains. Given the exacting computational requirements, cloud computing has been utilized to host user data and model. However, such an approach has potential privacy implications. Therefore, in this paper, we propose a method to protect user’s privacy in the inference phase of deep learning workflow. Specifically, we use an intermediate layer to separate the entire neural network into two parts, which are respectively deployed on the user device and the cloud server. The encoder, deployed on the user device, is used for raw data transformation, which removes the need for users to upload raw data to the cloud directly. However, we also demonstrate there exists potential for privacy leakage in the intermediate features of the neural network through two concrete experiments. In other words, the encoder on its own does not provide adequate privacy protection. Therefore, we also propose an approach to achieve Privacy-preserving Feature Extraction based on Adversarial Training (P-FEAT), where the goal of privacy attacking tasks and the goal of target tasks are adversarial in terms of sensitive attributes. By imposing privacy constraints during the feature extraction, we can reduce the contribution of the extracted features to the privacy leakage. In this way, privacy protection capability of the encoder can be further strengthened. We then demonstrate the effectiveness of P-FEAT using a large number of experiments, whose findings show that P-FEAT can significantly reduce the threats of privacy attacking tasks while maintaining high accuracy of the target tasks. [ABSTRACT FROM AUTHOR]