8 results on '"RECOMMENDER systems"'
Search Results
2. A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions.
- Author
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TIANZI ZANG, YANMIN ZHU, HAOBING LIU, RUOHAN ZHANG, and JIADI YU
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DEEP learning , *RECOMMENDER systems , *TAXONOMY - Abstract
Traditional recommendation systems are faced with two long-standing obstacles, namely data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Since the early 2010s, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey article, we first proposed a two-level taxonomy of cross-domain recommendation that classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field. [ABSTRACT FROM AUTHOR]
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- 2023
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3. A Revisiting Study of Appropriate Offline Evaluation for Top-N Recommendation Algorithms.
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WAYNE XIN ZHAO, ZIHAN LIN, ZHICHAO FENG, PENGFEI WANG, and JI-RONG WEN
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ALGORITHMS , *RECOMMENDER systems , *DEEP learning , *ONLINE algorithms - Abstract
In recommender systems, top-N recommendation is an important task with implicit feedback data. Although the recent success of deep learning largely pushes forward the research on top-N recommendation, there are increasing concerns on appropriate evaluation of recommendation algorithms. It therefore is important to study how recommendation algorithms can be reliably evaluated and thoroughly verified. This work presents a large-scale, systematic study on six important factors from three aspects for evaluating recommender systems. We carefully select 12 top-N recommendation algorithms and eight recommendation datasets. Our experiments are carefully designed and extensively conducted with these algorithms and datasets. In particular, all the experiments in our work are implemented based on an open sourced recommendation library, Recbole [139], which ensures the reproducibility and reliability of our results. Based on the large-scale experiments and detailed analysis, we derive several key findings on the experimental settings for evaluating recommender systems. Our findings show that some settings can lead to substantial or significant differences in performance ranking of the compared algorithms. In response to recent evaluation concerns, we also provide several suggested settings that are specially important for performance comparison. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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4. Selecting and Composing Learning Rate Policies for Deep Neural Networks.
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WU, YANZHAO and LIU, LING
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ARTIFICIAL neural networks , *RECOMMENDER systems - Abstract
The choice of learning rate (LR) functions and policies has evolved from a simple fixed LR to the decaying LR and the cyclic LR, aiming to improve the accuracy and reduce the training time of Deep Neural Networks (DNNs). This article presents a systematic approach to selecting and composing an LR policy for effective DNN training to meet desired target accuracy and reduce training time within the pre-defined training iterations. It makes three original contributions. First, we develop an LR tuning mechanism for auto-verification of a given LR policy with respect to the desired accuracy goal under the pre-defined training time constraint. Second, we develop an LR policy recommendation system (LRBench) to select and compose good LR policies from the same and/or different LR functions through dynamic tuning, and avoid bad choices, for a given learning task, DNN model, and dataset. Third, we extend LRBench by supporting different DNN optimizers and show the significant mutual impact of different LR policies and different optimizers. Evaluated using popular benchmark datasets and different DNN models (LeNet, CNN3, ResNet), we show that our approach can effectively deliver high DNN test accuracy, outperform the existing recommended default LR policies, and reduce the DNN training time by 1.6-6.7× to meet a targeted model accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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5. Position-Enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations.
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LIWEI HUANG, YUTAO MA, YANBO LIU, BOHONG DANNY DU, SHULIANG WANG, and DEYI LI
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DEEP learning , *RECURRENT neural networks , *BIPARTITE graphs , *RECOMMENDER systems - Abstract
The sequential recommendation (also known as the next-item recommendation), which aims to predict the following item to recommend in a session according to users’ historical behavior, plays a critical role in improving session-based recommender systems. Most of the existing deep learning-based approaches utilize the recurrent neural network architecture or self-attention to model the sequential patterns and temporal influence among a user’s historical behavior and learn the user’s preference at a specific time. However, these methods have two main drawbacks. First, they focus on modeling users’ dynamic states from a user-centric perspective and always neglect the dynamics of items over time. Second, most of them deal with only the first-order user-item interactions and do not consider the high-order connectivity between users and items, which has recently been proved helpful for the sequential recommendation. To address the above problems, in this article, we attempt to model user-item interactions by a bipartite graph structure and propose a new recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) for the sequential recommendation. PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation and learning the dynamic representations of users and items simultaneously on the bipartite graph with a self-attention aggregator. Also, it realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions. To demonstrate the effectiveness of PTGCN, we carried out a comprehensive evaluation of PTGCN on three real-world datasets of different sizes compared with a few competitive baselines. Experimental results indicate that PTGCN outperforms several state-of-the-art sequential recommendation models in terms of two commonly-used evaluation metrics for ranking. In particular, it can make a better trade-off between recommendation performance and model training efficiency, which holds great potential for online session-based recommendation scenarios in the future. [ABSTRACT FROM AUTHOR]
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- 2023
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6. Point-of-Interest Recommender Systems Based on Location-Based Social Networks: A Survey from an Experimental Perspective.
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SÁNCHEZ, PABLO and BELLOGÍN, ALEJANDRO
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RECOMMENDER systems , *SOCIAL networks , *INFORMATION resources , *DEEP learning , *EVALUATION methodology - Abstract
Point-of-Interest recommendation is an area of increasing research and development interest within the widely adopted technologies known as Recommender Systems. Among them, those that exploit informa)tion coming from Location-Based Social Networks are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done over the past 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportuni)ties and challenges that remain open in the field. More specifically, we report on the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also examine the lack of reproducibility in the field that may hinder real performance improvements. [ABSTRACT FROM AUTHOR]
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- 2022
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7. Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems.
- Author
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LIANGHAO XIA, CHAO HUANG, YONG XU, HUANCE XU, XIANG LI, and WEIGUO ZHANG
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DEEP learning , *RECOMMENDER systems , *INFORMATION networks , *MISSING data (Statistics) - Abstract
As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such as multi-layer perceptron, autoencoder, and graph neural networks. However, the majority of existing collaborative filtering systems are not well designed to handle missing data. Particularly, in order to inject the negative signals in the training phase, these solutions largely rely on negative sampling from unobserved user-item interactions and simply treating them as negative instances, which brings the recommendation performance degradation. To address the issues, we develop a Collaborative Reflection-Augmented Autoencoder Network (CRANet), that is capable of exploring transferable knowledge from observed and unobserved user-item interactions. The network architecture of CRANet is formed of an integrative structure with a reflective receptor network and an information fusion autoencoder module, which endows our recommendation framework with the ability of encoding implicit user’s pairwise preference on both interacted and non-interacted items. Additionally, a parametric regularizationbased tied-weight scheme is designed to perform robust joint training of the two-stage CRANet model. We finally experimentally validate CRANet on four diverse benchmark datasets corresponding to two recommendation tasks, to show that debiasing the negative signals of user-item interactions improves the performance as compared to various state-of-the-art recommendation techniques. [ABSTRACT FROM AUTHOR]
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- 2022
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8. Route Optimization via Environment-Aware Deep Network and Reinforcement Learning.
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PENGZHAN GUO, KELI XIAO, ZEYANG YE, and WEI ZHU
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DEEP reinforcement learning , *REINFORCEMENT learning , *COVID-19 pandemic , *RECOMMENDER systems , *SMART cities , *CITIES & towns - Abstract
Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring. To account for unexpected situations (e.g., the COVID-19 outbreak), our method is designed to be capable of handling related environment changes with a self-adaptive parameter determination mechanism. Based on the yellow taxi data in New York City and vicinity before and after the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate the effectiveness of our method. The results show consistently excellent performance, from hourly to weekly measures, to support the superiority of our method over the state-of-the-art methods (i.e., with more than 98% improvement in terms of the profitability for taxi drivers). [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
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