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One-class collaborative filtering (OCCF) problems are prevalent in real-world recommender systems, such as news and music recommendation, but suffer from sparsity issues and particularly lack negatively preferred items. To address the challenge, the state-of-the-art algorithms assigned uninteracted items smaller weights of being negative and performed low-rank approximation over a user-item interaction matrix. However, the prior ratings of uninteracted items are usually set to zero but may not well-defined. To avert the prior ratings of uninteracted items, in this paper, we propose a novel ranking-based implicit regularizer by hypothesizing that users preference scores for uninteracted items are close to each other. The regularizer is then used in a ranking-based OCCF framework to penalize large difference of preference scores between uninteracted items. To optimize model parameters in this framework, we develop a scalable alternating least square algorithm and coordinate descent algorithm, whose time complexity is linearly proportional to data size. Finally, we extensively evaluate the proposed algorithms with six real-world datasets. The results show that the proposed regularizer significantly improves recommendation quality of ranking-based OCCF algorithms, such as BPRMF and RankALS. Moreover, the ranking-based framework with the proposed regularizer outperforms the state-of-the-art recommendation algorithms for implicit feedback.

Multiple cooperative task assignment is a crucial problem in spatial crowdsourcing, in which each task requires more than one appropriate user to complete. Existing studies generally assume that all users are trustworthy and can reliably perform assigned tasks. However, such assumptions do not hold in the real world. In this paper, we consider an essential crowdsourcing problem, namely Reliability-oriented Socially-Aware Crowdsourcing (R-SAC), which aims to recruit reliable users for multiple cooperative tasks so that the overall reliability of task assignment is maximized. We prove that the R-SAC problem is NP-hard. Then, we propose an approximation algorithm and provide a theoretical guarantee. Specifically, user reliability refers to the probability that a user can reliably perform assigned tasks. To achieve reliable user recruitment during task assignment, we formulate the reliability of a user by combining the matching between the user and tasks, and the reliability feedback from neighbors who share similar behaviors with the user in a social network. Besides, the distributed collaborative filtering technique is utilized to select the reliability feedback from the neighbors. Experiments on two real-world datasets validate that our proposed approach significantly outperforms the representative approaches.

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\% on hit ratio (HR) and normalized discounted cumulative gain (NDCG) compared with the baselines.

Citation recommendation is important in the environment of scholarly big data, where finding relevant papers has become more difficult because of information overload. Applying traditional collaborative filtering (CF) to citation recommendation is rather challenging due to the cold start problem and the lack of paper ratings. To address these two challenges, in this paper, we propose a Collaborative filtering with Network representation learning framework for Citation Recommendation dubbed as CNCRec, which is a hybrid user-based CF considering both paper content and network topology. It aims at recommending citations in heterogeneous academic information networks. CNCRec creates the paper rating matrix based on attributed citation network representation learning, where the attributes are topics extracted from the paper text information. Meanwhile, the learned representations of attributed collaboration network is utilized to improve the selection of nearest neighbors. By harnessing the power of network representation learning, CNCRec is able to make full use of the whole citation network topology compared with previous context-aware network-based models. Extensive experiments on both DBLP and APS datasets show that the proposed method outperforms state-of-the-art methods in terms of precision, recall, and Mean Reciprocal Rank. Moreover, CNCRec can better solve the data sparsity problem compared with other CF-based baselines.

A recommendation system saves the user the trouble of searching for information and analyzes their profile to recommend the most suitable content. A variety of techniques, including content-based, collaborative, and knowledge-based techniques, have been proposed for performing recommendations. Recommendation systems are employed to suggest content, such as books, music, and videos; furthermore, they are often used in e-commerce. In particular, the South Korean film industry recommends movies using a collaborative filtering method based on genres, which is commonly used in film recommendation systems. However, this method can be ineffective when users initially encounter film recommendation services or have specific preferences with respect to movies, such as preferences regarding actors or directors. This motivated us to propose an actor-based recommendation system using the content-based filtering that considers actors' filmography information and the genres of 509 South Korean movies. The effectiveness and performance of the suggested system are evaluated in comparison with those of a traditional genre-oriented recommendation system.

Now a day’s recommendation system take care of the issue of the massive amount of information overload problem and it provides the services to the candidates to concentrate on relevant information on job domain only. The job recommender system plays an important role in the recruitment process of fresher as well as experienced today. Existing job recommender system mainly focuses on content-based filtering to extricate profile content and on collaborative filtering to capture the behaviour of the user in the form of rating. Dynamic nature of job market leads cold start and scalability issues. This problem can be addressed by item-based collaborative filtering with a machine learning technique, it learns job embedding vector and finds similar jobs content-wise. Existing model in job recommender domain uses the confining model to address the cold start and scalability issue and provide better recommendation, but they fail to accept the complex relationships between job description and candidate profile. In this paper, we are proposing a Deep Semantic Structure Algorithm that overcome the issue of the existing system. Deep semantic structure modelling (DSSM) system uses the semantic representation of sparse data and it represent the job description and skill entities in character trigram format which increases the efficacy of the system. We are comparing the results to three variation of DSSM model with two different dataset (Naukari.com and CareerBuilder. com) and it gives satisfactory results. Experimental results shows that the DSSM Embedding model and its other variants are provides promising results in solving cold start problem in comparison with several variants of embedding model. We used Xavier initializer to initialise the model parameter and Adam optimizer to optimize the system performance.

Visual media, in today’s world, has swept across most forms of day to day communication. In the paradigm of generative modelling, restricted Boltzmann machines (RBMs) are used to solve complex tasks such as feature extraction, neuroimaging, collaborative filtering, radar target cognition, etc. In this paper, we implement an unsupervised Boltzmann machine-based time-aware recommendation system (UBMTR) which detects underlying hidden features in user-movie ratings data in connection with the time at which each rating was made (temporal information). The model takes ratings and time as a dual-input and outputs binary values via the contrastive divergence algorithm which samples from a Monte Carlo Markov Chain. Arguably, there exists a correlation between the content requested and the temporal conditions, which is exactly what our model tries to exploit. There is seldom any work in the field of recommender systems built using Boltzmann machines that incorporate temporal information, which necessitates research in this domain. RBMs are adept at pattern completion to tackle missing values, and can deal with imbalanced datasets and unstructured data by encoding raw data into latent variables. Using RBM, the UBMTR outperforms many earlier made attempts made at recommendation systems done through CF and deep learning or their hybridized models.

The ever-increasing popularity of recommendation systems allows users to find appropriate services without excessive effort. However, due to the unstable and complex network environment, the historical behavior data of users are quite sparse in most cases. The inherent drawbacks render preference prediction infeasible for cold-start users and have become a crucial issue to be resolved in recommendation systems. To deal with the problems, we first present a Trust-based Collaborative Filtering (TbCF) algorithm to perform basic rating prediction in a manner consistent with the existing CF methods. Then, we propose the Hybrid Collaborative Filtering Recommendation approach with User-Item-Trust Records (UIThybrid), a novel approach that incorporates user trust into the existing CF-based methods in a harmonious way to supplement rating information. UIThybrid employs multiple perspectives to extract proper services and achieves a good tradeoff between the robustness, accuracy, and diversity of the recommendation. We conduct extensive real-world experiments on the Epinions data set to demonstrate the feasibility and efficiency of UIThybrid.

Modern resource management frameworks guarantee low tail latency for long-running services using the resource over-provisioning method, resulting in serious waste of resources and increasing the service costs greatly. To reduce the over-provisioning cost, we present HRSE, a hybrid resource scaling engine that enables much more efficient resource provisioning for both periodic and non-periodic workloads of long-running services while guaranteeing the tail latency Service Level Objective (SLO). HRSE employs a convolution-based time series analysis to identify periodic patterns in workloads. If periodic patterns are discovered, HRSE estimates the just-right amount of resources based on the periodic features through a top-K based collaborative filtering approach. Otherwise, it leverages wavelet-clustering to capture the short-term patterns in non-periodic workloads and predict the resource demands for the near future. To further enforce the tail latency SLO, HRSE uses an online reprovisioning mechanism that dynamically adjusts the resources to mitigate the performance uncertainty due to workload burstinesses. We fully implement HRSE on top of Docker and conduct extensive experiments using traces from production systems. Testbed experiments show that HRSE is able to increase the average resource utilization to 43% and 45% for periodic and non-periodic workloads respectively while guaranteeing the same tail latency objective.

Nowadays, there are many Web services with similar functionality on the Internet. Users consider Quality of Service (QoS) of the services to select the best service from among them. The prediction of QoS values of the Web services and recommendations of the best service based on these values to the users is one of the major challenges in the web service area. Major studies in this field use collaboration filtering based methods for prediction. The paper introduced prediction methods and divided them into three main categories: memory-based methods, model-based methods, and Collaborative Filtering (CF) methods combined with other methods. In each category, some of the most famous studies were introduced, and then the problems and benefits of each category were reviewed. Finally, we have a discussion about these methods and propose suggestions for future works.

Extensive growth in developing new and efficient methods for tensor factorizations has made their intelligent applications in cyber-physical-social systems (CPSS) a hot research topic. Tensor factorizations facilitate the need for recommendations that are accurate and circumstantial, which pushes the limits of traditional collaborative filtering methods to multifaceted versions based on real intelligent environments. Nevertheless, recommenders in edge-cloud computing require information encapsulated in user models to give useful suggestions on user preferred items, which presents stern privacy trepidations. In this paper, a novel edge-cloud-aided differentially private tucker decomposition scheme is proposed to avert data owner’s private data from being learned by other data owners, untrusted edge and cloud during tucker decomposition for CPSS. Our design dissevers users’ private data computations in tucker decomposition to edges from the cloud, and the cloud is forced to perform perturbed results aggregation while preserving privacy. The scheme employs perturbation to ensure differential privacy, and the perturbation noise components are decomposed into small manageable parts that can be locally and independently resolved by edges. Our extensive experiments on two real datasets show the proposed scheme is efficient and has tolerable side-effects on the results’ utility.

Recommendation systems (RS) play an important role in e-commerce applications as they help the consumers in choosing the required items within reduced time. The traditional methods of collaborative filtering, fail to capture the visual data associated with the items. Visually-aware recommendation systems are upcoming in e-commerce applications that use the visual features of the products rather than the user profiles. Deep learning techniques are used for the classification and prediction in visual recommendation systems. However, the criticality of the visual recommendation system lies in identifying the similar images for a given target image. In this paper, a visual recommendation system is proposed based on Deep Visual Ensemble similarity metric (DVESM) using Convolutional Autoencoder (CAE) neural network for classification. The basic idea is to get a set of trained feature vectors for the image catalogue using CAE and find the similarity between the trained feature vectors and the target feature vector using DVESM method. The proposed methodology of using DVESM has been demonstrated with the state-of-the-art methods on Amazon 2014, 2015 and Street2Shop datasets. The results show that the DVESM method is most suitable for visually aware recommendation systems as it learns an ensemble of metrics to provide recommendations.

Nowadays, most data about social networks, users shopping behaviors, and inter-item relationships etc., can be represented as graph structure. Graph Neural Networks (GNNs) have shown great success in learning meaningful representations for graph data by naturally integrating node information and topological structures. Data used in making social recommendations can also be represented as graph data in the form of user-user social graphs and user-item graphs. In addition, the relationships between items can be represented as graph data, denoted as item-item graph. GNNs provide an unprecedented opportunity to advance social recommendations, yet there are considerable challenges in building GNNs-based social recommendations based on this modelling framework in that (1) users (items) are simultaneously involved in the user-item graph and user-user social graph (item-item graph); (2) user-item graphs not only contain user-item interactions but also include users' opinions on items; and (3) the nature of social relations are heterogeneous among users. In this paper, we propose a novel graph neural network framework (GraphRec+) for social recommendations, which is able to coherently model graph data in order to learn better user and item representations. Comprehensive experiments on three real-world datasets show the effectiveness of the proposed framework.

Nowadays, the Internet is becoming a platform of choice where the number of users and items grows dramatically making recommender systems (RS) the most required and widespread technology. This paper deals with context aware collaborative RS and presents a double contribution that consists of a two Dimensions Contextual Collaborative Recommender System (2DCCRS) and a related application. Our first contribution proposes a new framework for collaborative context aware RS that relies on two key ideas. The first one suggests splitting the context into two parts namely internal and external contexts in order to deal with both internal and external context attributes in different and more appropriate manners. This allows addressing the complexity of the context model in a more effective way. In the second idea, we introduce two concepts; namely the “Stakeholders” and “Aggregation” to effectively alleviate the problems of new user and new item. 2DCCRS is based on a multi-layer architecture. Its highest layer relies on a pre-filtering algorithm that deals with the cold start system problem, and is mainly based on the similarity between the user profile and the items features. The middle layer is based on a collaborative filtering algorithm that takes into account the users’ preferences, interests and priorities; while the deepest layer, which is considered the most relevant in our multilayer architecture, focuses on a post-filtering algorithm in which the recommendations are much more adapted to the user environment. In Our second contribution, we present a case study of 2DCCRS in order to demonstrate the usefulness and effectiveness of the proposed approach. Indeed, we propose a personalized Healthy and Tasty application (H&T) that generates items based on 2DCCRS framework to guide the user toward the healthy and tasty meals that best meet his needs. The obtained results are very promising and show the effectiveness of our proposal.

An effective recommender system can significantly help customers to find desired products and assist business owners to earn more income. Nevertheless, the decision-making process of users is highly complex, not only dependent on the personality and preference of a user, but also complicated by the characteristics of a specific product. For example, for products of different domains (e.g., clothing vs. office products), the product aspects that affect a user's decision are very different. As such, traditional collaborative filtering methods that model only user-item interactions would deliver unsatisfactory recommendation results. In this work, we first divide a product's characteristics into visual and functional aspects. We then contribute a novel probabilistic model, named Visual and Functional Probabilistic Matrix Factorization (VFPMF), to unify the two factors to estimate user preferences on products. Nevertheless, such an expressive model poses efficiency challenge in parameter learning from implicit feedback. To address the technical challenge, we devise a computationally efficient learning algorithm for VFPMF. Furthermore, we provide an online updating procedure of the algorithm, shedding some light on how to adapt our method to real-world recommendation scenario where data continuously streams in. Extensive experiments on four datasets demonstrate the effectiveness of our method.

We propose a novel privacy-preserving QoS forecasting approach - Edge-Laplace QoS (QoS forecasting with Laplace noise in mobile Edge environments). Edge-Laplace QoS is able to accurately and efficiently forecast Quality of Service (QoS) of various Web Services, while effectively protecting user privacy in mobile edge environments. We employ an improved differential privacy method to add dynamic disguises to the original QoS data in the edge environment to protect user data privacy. A collaborative filtering method is adopted to retrieve similar users' accessing records based on geographic locations of their accessed servers for QoS forecasting. We conduct a set of experiments using several public network data sets. The results show that the efficiency of Edge-Laplace QoS is superior to traditional forecasting approaches. Edge-Laplace QoS is also validated to be more suitable for edge environments than traditional privacy-preserving approaches.

Background: The prediction of drug-protein interaction (DPI) plays an important role in drug discovery and repositioning. Unfortunately, traditional experimental validation of DPIs is expensive and time-consuming. Therefore, it is necessary to develop in silico methods for the identification of potential DPIs. Method: In this work, the identification of DPIs was performed by the generated recommendation of the unexplored interaction of the drug-protein bipartite graph. Three kinds of recommenders were proposed to predict the potential DPIs. Results: The simulation results showed that the proposed models obtained good performance in crossvalidation and independent test. Conclusion: Our recommendation strategy based on collaborative filtering can effectively improve the DPI identification performance, especially for certain DPIs lacking chemical structure similarity or genomic sequence similarity.

Food recommendation has attracted increasing attentions to various food-related applications and services. The food recommender models aim to match users’ preferences with recipes, where the key lies in the representation learning of users and recipes. However, ranging from early content-based filtering and collaborative filtering methods to recent hybrid methods , the existing work overlooks the various food-related relations, especially the ingredient-ingredient relations, leading to incomprehensive representations. To bridge this gap, we propose a novel model Food recommendation with Graph Convolutional Network (FGCN), which exploits ingredient-ingredient, ingredient-recipe, and recipe-user relations deeply. FGCN employs the information propagation mechanism and adopts multiple embedding propagation layers to model high-order connectivity across different food-related relations and enhance the representations. Specifically, we develop three types of information propagation: (1) ingredient-ingredient information propagation, (2) ingredient-recipe information propagation, and (3) recipe-user information propagation. To validate the effectiveness and rationality of FGCN, we conduct extensive experiments on a real-world dataset. The results show that the proposed FGCN outperforms the state-of-the-art baselines. Further in-depth analyses reveal that FGCN could alleviate the sparsity issue in food recommendation.

There are large personalized differences among human resources, and the resource recommendation process easily encounters the problem of data loss, resulting in a short length of the final recommended resource and less information about the actual human resources included. To solve this problem, this research proposes a human resource recommendation algorithm based on improved frequent itemset mining. The dataset containing single items is called, the human resource is mined to improve frequent itemset, the positive matrix factorization (PMF) model is used to process the implicit features in human resource, and the similarity parameters of the human resource dataset are calculated. The algorithm of collaborative filtering is used to divide the recommendation mechanism, and the loss of data generated during the recommendation is controlled, thereby completing the construction of the human resource recommendation algorithm. The hardware and software environment for algorithm operation were constructed. The human resource dataset was arranged. Tests were conducted using the recommendation algorithm based on neural network, recommendation algorithm based on item weight ranking and the proposed recommendation algorithm. The test results showed that the proposed recommendation algorithm recommended the longest length of resources and the most information contained within the resources.

Template matching between synthetic aperture radar (SAR) and optical image is a meaningful and important topic for remote sensing tasks. In this letter, a deep learning framework based on Siamese structure and U-Net is proposed. First, a sparse 3-D transform-domain collaborative filtering (BM3D) for SAR image denoising is applied. Then, deep features of SAR and optical images are extracted from two branches of the proposed Siamese U-Net network. Finally, a cross correlation layer is used to generate the heatmap of image pairs based on the deep features extracted, and the peak maximal location of the heatmap is seen as the best matching location result. During training, a balanced loss between ground truth and prediction is used to get trained model weights in order to make a balance between positive samples and negative samples. The experiments on the same test dataset demonstrate that the proposed method improves the matching accuracy and precision to some degree in comparison with existing template matching methods for SAR and optical images.

Implicit feedback is widely used in collaborative filtering methods for sequential recommendation. It is well known that implicit feedback contains a large number of values that are missing not at random (MNAR); and the missing data is a mixture of negative and unknown feedback, making it difficult to learn users' negative preferences. Recent studies modeled exposure, a latent missingness variable which indicates whether an item is exposed to a user, to give each missing entry a confidence of being negative feedback. However, these studies use static models and ignore the information in temporal dependencies among items, which seems to be an essential underlying factor to subsequent missingness. To model and exploit the dynamics of missingness, we propose a latent variable named "user intent" to govern the temporal changes of item missingness, and a hidden Markov model to represent such a process. The resulting framework captures the dynamic item missingness and incorporates it into matrix factorization (MF) for recommendation. We further extend the proposed framework to capture the dynamic preference of users, which results in a unified framework that is able to model different evolution patterns of user intent and user preference. We also explore two types of constraints to achieve a more compact and interpretable representation of user intents. Experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommender systems.

Here we are building an collaborative filtering matrix factorization based hybrid recommender system to recommend movies to users based on the sentiment generated from twitter tweets and other vectors generated by the user in their previous activities. To calculate sentiment data has been collected from twitter using developer APIs and scrapping techniques later these are cleaned, stemming, lemetized and generated sentiment values. These values are merged with the movie data taken and create the main data frame.The traditional approaches like collaborative filtering and content-based filtering have limitations like it requires previous user activities for performing recommendations. To reduce this dependency hybrid is used which combines both collaborative and content based filtering techniques with the sentiment generated above. Keywords: machine learning, natural language processing, movie lens data, root mean square equation, matrix factorization, recommenders system, sentiment analysis

The Neighbourhood-based collaborative filtering (CF) algorithm has been widely used in recommender systems. To enhance the adaptability to the sparse data, a CF with new similarity measure and prediction method is proposed. The new similarity measure is designed based on the Hellinger distance of item labels, which overcomes the problem of depending on common-rated items (co-rated items). In the proposed prediction method, we present a new strategy to solve the problem that the neighbour users do not rate the target item, that is, the most similar item rated by the neighbour user is used to replace the target item. The proposed prediction method can significantly improve the utilisation of neighbours and obviously increase the accuracy of prediction. The experimental results on two benchmark datasets both confirm that the proposed algorithm can effectively alleviate the sparse data problem and improve the recommendation results.

This paper is aimed at studying the online education and wireless network collaboration on electronic music creation and performance under artificial intelligence (AI). This paper uses a fuzzy clustering algorithm (FCA), designs the sensor network-related equipment, and uses AI to design an electronic music creation system. The analysis of simulation experiments suggests that under the premise of increasing the number of neighbors, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) of collaborative filtering and fuzzy C -means clustering algorithms show a downward trend. However, with the same number of neighbors, the filtering matching algorithm is greater than FCA regarding the mean values of MAE and MSE. Meanwhile, on the electronic music performance system of AI, the digital module is designed and the sound data are imaged on the oscilloscope, and the collaboration of electronic music online education and wireless network is completed. The following conclusion is drawn: modularizing the creative mode of intelligent electronic music has achieved higher computational efficiency. Through the oscilloscope, the sound feature is converted into the image structure, and the corresponding sound and image mode is formed, which realizes the purpose of online electronic music intelligent matching and optimizes the effect of online education. In the AI environment, the matching degree of verification electronic music curriculum resources is better than traditional matching algorithms, and the accuracy is higher.

In the recommender system, the user’s historical behavior data is one of the most important sources of the system’s input data. According to the user’s feedback mechanism, behavior data can be divided into explicit feedback data and implicit feedback data. However, most recommendation algorithms focus separately on explicit feedback or implicit feedback. How to combine explicit and implicit feedback for recommendation tasks has always been a research problem. In recent years, deep learning technology has dominated the research on recommendation algorithms. But even the latest neural network-based recommendation algorithm cannot exceed classic methods (such as matrix factorization) in most cases. In this work, we propose a new collaborative filtering framework with neural network architecture. On the one hand, we use both explicit feedback data and implicit feedback data as input to learn multiple representations of users and items. On the other hand, we use multi-task learning to optimize our framework and use two relatively simple auxiliary tasks to enhance the generalization ability of our framework. Extensive experiments on five real-world datasets show significant improvements in our proposed framework over the state-of-the-art methods and vanilla matrix factorization.

It is necessary to solve the inaccurate data arising from data reliability ignored by most data fusion algorithms drawing upon collaborative filtering and fuzzy network theory. Therefore, a model is constructed based on the collaborative filtering algorithm and fuzzy network theory to calculate the node trust value as the weight of weighted data fusion. First, a FTWDF (Feedback Trust Weighted for Data Fusion) is proposed. Second, EEFA (Efficiency unequal Fuzzy clustering Algorithm ) is introduced into FTWDF considering the defects of the clustering structure caused by ignoring the randomness of node energy consumption and cluster head selection in the practical application of the existing data fusion algorithm. Besides, the fuzzy logic is applied to cluster head selection and node clustering. Finally, an FTWDF-EEFA clustering algorithm is constructed for generating candidate cluster head nodes, which is verified by simulation experiments. The comparative analysis reveals that the accuracy of the FTWDF-EEFA clustering algorithm is 4.1% higher than that of the TMDF (Trust Multiple attributes Decision-making-based data Fusion) algorithm, and 8.3% higher than that of LDTS ( Larger Data fusion based on node Trust evaluation in wireless Sensor networks) algorithm. It performs better in accuracy and recommendation results during the processing of ML100M dataset and NF5M dataset. Besides, the new clustering algorithm increases the survival time of nodes when analyzing the number of death nodes to prolong networks’ lifespan. It improves the survival period of nodes, balances the network load, and prolongs networks’ lifespan. Furthermore, the FTWDF-EEFA clustering algorithm can balance nodes’ energy consumption and effectively save nodes’ overall energy through analysis. Therefore, the optimized algorithm can increase the lifespan of network and improve the trust mechanism effectively. The performance of the algorithm has reached the expected effect, providing a reference for the practical application of the trust mechanism in networks.

Memory-based collaborative filtering constitutes an important technique of recommender systems mainly due to its simplicity and efficiency. However, it suffers from several fundamentally critical problems when its system makes recommendations based on ratings records of similar users. This study addresses data sparsity and scalability problems that are major drawbacks of the memory-based system. In order to take care of the data sparsity problem, we deduce user interest in movie genres from the user ratings and devise a similarity measure based on the genre preference. Then clusters of users are built based on the genre preference similarity by employing a fuzzy clustering technique, which not only reflects the subjectivity of user ratings but reduces the data scalability problem. Furthermore, we apply an optimization method to the proposed technique to resolve shortcomings of the fuzzy clustering algorithm by using the genetic algorithm. Extensive experiments are conducted to find that the proposed method demonstrates its performance superior or comparable to the previous methods in terms of various metrics. Moreover, the proposed approach turns out to yield the highest prediction accuracy among the experimented methods, thus proving to overcome the serious problem of low prediction encountered with clustering-based collaborative filtering.

Mobile edge computing (MEC) is an emerging technolohgy that extends cloud computing to the edge of a network. MEC has been applied to a variety of services. Specially, MEC can help to reduce network delay and improve the service quality of recommendation systems. In a MEC-based recommendation system, users' rating data are collected and analyzed by the edge servers. If the servers behave dishonestly or break down, users' privacy may be disclosed. To solve this issue, we design a recommendation framework that applies local differential privacy (LDP) to collaborative filtering. In the proposed framework, users' rating data are perturbed to satisfy LDP and then released to the edge servers. The edge servers perform partial computing task by using the perturbed data. The cloud computing center computes the similarity between items by using the computing results generated by edge servers. We propose a data perturbation method to protect user's original rating values, where the Harmony mechanism is modified so as to preserve the accuracy of similarity computation. And to enhance the protection of privacy, we propose two methods to protect both users' rating values and rating behaviors. Experimental results on real-world data demonstrate that the proposed methods perform better than existing differentially private recommendation methods.

Background Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. Methods In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model. Results The experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease. Conclusion With the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.

As the number of cloud services (CSs) offering similar functionality is growing, more attention has been payed on the quality of service (QoS) of CSs. However, in a dynamic cloud environment, the explicit and inherent variation of QoS causes the single CS selection via collaborative filtering techniques (CSS-CFT) to be challengeable. A variation-aware approach via collaborative QoS prediction is proposed to select an optimal CS according to users' non-functional requirements. Based on time series QoS data, this approach utilizes a set of specific cloud models to quantify the variation characteristics of QoS from the four aspects including central tendency, variation range, frequency of variation and period. To exactly identify the neighboring users for a current user, this paper employs the double Mahalanobis distances to measure the similarity of QoS cloud models. The variation-aware CSS-CFT is formulated as a multi-criteria decision-making problem, and an improved TOPSIS method is exploited to solve it, by considering both the objective QoS variation and subjective user preferences during different time periods. The experiments based on a real-world dataset demonstrate that the proposed approach can enhance the accuracy of CSS-CFT in a high-variance environment without noticeable increase of selection time, in comparison to the existing approaches.

With the rapid development of cloud manufacturing (CMfg), a large number of functionally equivalent cloud services are available on the Internet, thus quality of service (QoS) prediction has become a hot issue. Since the QoS of service varies widely among users, many Collaborative Filtering (CF) approaches are recently proposed to predict the unknown QoS by extending the Pearson Correlation Coefficient (PCC) similarity. However, most existing approaches either enhance the similarity computation inadequately or ignore the data sparsity problem, and are thus vulnerable to the unreliable result. To address this problem, we propose a hybrid approach HAP for QoS prediction. Firstly, to achieve high prediction accuracy, we employ a similarity-enhanced CF (L-CF) for local QoS prediction, in which the personalized influence of similar users and services are considered when computing PCC similarity. Secondly, to overcome the data sparsity problem, we propose a global QoS prediction approach based on the case-based reasoning (G-CBR) which could make full use of both user and service information. Finally, we establish an ensemble model to combine the results of two approaches. Comprehensive real-world experiments are conducted to demonstrate the effectiveness of our approach compared with other state-of-the-arts approaches, particularly when the QoS data is very sparse.

With the rapid development of cloud computing technology, online recommendation services have been a new trend for providing specific information on topics such as lifestyle, fashion news and a variety of other activities. Recommendation algorithm is crucially important for the delivery of personalized services to users. Currently, the key important problem is how to improve the quality of online recommendation services. To solve the problem of goods popularity bias, this paper introduces the prevalence of items into user interest model, and proposes an item popularity model based on user interest feature. Usually, traditional models do not consider the stable of users’ interests, which may make it hard to capture their interest. To deal with the above situation, we propose a time-sensitive and stable interest similarity model to calculate the similarity of user interest. Furthermore, a novel algorithm termed as item popularity similarity with time sensitivity (IPSTS)is proposed by combining the above two kinds of similarity model, and they are assigned different weight factors to balance their impacts. In this paper, we conduct the comparative experiments to evaluate the proposed approach and the traditional collaborative filtering algorithms. The final experimental results indicate that IPSTS can effectively reduce the value of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

The vehicular ad hoc network (VANET) has become a hot topic in recent years. With the development of VANETs, how to achieve secure and efficient machine learning in VANETs is an urgent problem to be solved. Besides, how to ensure that users obtain the accurate results of machine learning is also a challenge. Based on the homomorphic encryption and secure multiparty computing technology, a flexible and privacy-preserving collaborative filtering scheme is proposed to accomplish the personalized recommendation for users, which is based on users’ interests and locations. On the one hand, the data can be updated by users flexibly to ensure the freshness and accuracy of the dataset of interest. On the other hand, the weighted values of user interest can be safely sorted to improve the accuracy of collaborative filtering effectively. Moreover, a novel collaborative filtering algorithm based on the homomorphic encryption technology is designed, which can guarantee that the calculated decryption result by machine learning is the same as the plaintext. Note that the privacy of user data can be preserved during machine learning in this algorithm. Both theoretical and experimental analyses demonstrate that the proposed scheme is secure and efficient for collaborative filtering in cloud computing in VANETs.

Aiming at the problems of low user satisfaction and long response time in traditional online English education platforms, this paper designs a web-based online English education platform and carries out relevant application tests. With the support of the web, the hardware and software of the platform are designed. The hardware of the platform is composed of student module, teacher module, administrator module, and database module. On this basis, the K-means clustering method is used to cluster the learner data in the web, to determine their English level, and the collaborative filtering algorithm is combined to recommend relevant course materials for learners so as to complete the platform software design. The application test results show that the platform has the advantages of superior function and security, high user satisfaction, and short response time and has certain practical application value.

Withthe rapid development of mobile Internet and wireless technologies, edge servers in mobile edge computing (MEC) provide a large number of mobile services to develop advanced and complex mobile applications. Quality of Service (QoS) is an essential indicator for recommendation and selection from such a huge number of candidate mobile services. However, QoS records are sparse in reality because users only invoke certain mobile services. How to predict missing QoS values is a key issue in operations related to mobile services. Some collaborative filtering (CF) methods have been used to solve QoS prediction problem of traditional Web services. Neverless, the high dimensionality, high sparsity and dynamic variability of QoS data in MEC limit the application of these methods. This paper proposes a temporal regularized tensor factorization method based on hybrid CF for QoS prediction of mobile services. The overall structure of QoS data is represented by constructing a user-service-time tensor model. As QoS data possesses certain characteristics of time series, a temporal regular term is introduced into CP decomposition to mine the relationship between QoS values in adjacent time intervals. The alternating least square (ALS) algorithm is used to optimize the approximate tensor iteratively. The low-rank estimation of the target tensor is obtaind to predict missing values. Extensive experimental studies on the large-scale QoS dataset WS-Dream show that our method has significantly better prediction accuracy than the other nine state-of-the-art methods under different data densities. Especially in the case of sparse data, the prediction effect is more prominent, and the accuracy increases up to 62.4% at most.

PurposeThis paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.Design/methodology/approachFirst, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.FindingsFolksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.Originality/valueThe proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

The rapid development of the tourism industry and the Internet era has led to an increasingly severe problem of travel information overload, and travel recommendation methods are essential to solving the information overload problem. Traditional recommendation algorithms only target common travel scenarios during the daytime, combining the ratings and necessary attributes between users and items to calculate similarity for a recommendation. Still, the research on night travel recommendations is one of the few scenarios that needs to be explored urgently. This paper, based on histogram equalization, achieves better experimental results on image enhancement, combines convolutional neural network technology with night image and text comment feature extraction technology, and evaluates the resulting error with mean absolute error (MAE). This paper presents the first night travel recommendation system. It compares it with the traditional collaborative filtering method, and the model proposed in this paper can effectively reduce the prediction error.

In the context of recommender systems based on collaborative filtering (CF), obtaining accurate neighborhoods of the items of the datasets is relevant. Beyond particular individual recommendations, knowing these neighbors is fundamental for adding differentiating factors to recommendations, such as explainability, detecting shilling attacks, visualizing item relations, clustering, and providing reliabilities. This paper proposes a deep learning architecture to efficiently and accurately obtain CF neighborhoods. The proposed design makes use of a classification neural network to encode the dataset patterns of the items, followed by a generative process that obtains the neighborhood of each item by means of an iterative gradient localization algorithm. Experiments have been conducted using five popular open datasets and five representative baselines. The results show that the proposed method improves the quality of the neighborhoods compared to theK-Nearest Neighbors (KNN) algorithm for the five selected similarity measure baselines. The efficiency of the proposed method is also shown by comparing its computational requirements with that of KNN.

Recently, listwise collaborative filtering (CF) algorithms are attracting increasing interest due to their efficiency and prediction quality. Different from rating-oriented (pointwise) CF, they recommend a preference ranking of items to each user without estimating the absolute value of the ratings. In practice, there are extensive side information about users and items in many corpora that can improve recommendation performance and specially help CF methods to address the cold-start problem. However, a model has not been proposed that incorporates side information to listwise CF. Therefore, in this work, a Bayesian graphical model, called Content-Aware Listwise Collaborative Filtering (CALCF), is developed which incorporates text information, represented as a bag of words, to listwise CF using topic models. We propose a Gibbs sampler with closed-form samples using data augmentation techniques to infer the latent variables. CALCF has been validated by comparison with previous listwise and pointwise algorithms on implicit (click, view, purchase) and explicit (numeric ratings) feedback data in both cold-start and warm-start scenarios. The results demonstrate that in most cases CALCF achieves better Normalized Discounted Cumulative Gain (NDCG) and Recall at top M recommendation compared with the previous models.

The creation of digital marketing has enabled companies to adopt personalized item recommendations for their customers. This process keeps them ahead of the competition. One of the techniques used in item recommendation is known as item-based recommendation system or item–item collaborative filtering. Presently, item recommendation is based completely on ratings like 1–5, which is not included in the comment section. In this context, users or customers express their feelings and thoughts about products or services. This paper proposes a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users’ reviews and comments, without disrupting it. We have implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with [Formula: see text] accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of [Formula: see text], precision at [Formula: see text], recall at [Formula: see text] and F1-Score at [Formula: see text]. Our model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall. Results of this experiment indicate that the similarity recommendation algorithm performs better than the conventional algorithm and enhances recommendation accuracy.

People’s cognition of objectively existing things is developing in the direction of digitization, and more and more objectively existing things are constantly being presented in the form of data. In order to realize the analysis of constantly changing large-scale data and the automatic extraction of valuable information, this paper analyzes the application of artificial intelligence auxiliary equipment based on machine learning in the cognitive learning process, and proposes a collaborative filtering method based on fusion of global and local parameters. Moreover, this paper studies a cognitive computing model based on context-aware data stream to realize effective analysis of context-aware data and obtain effective cognitive results. In addition, this paper proposes a task scheduling algorithm based on improved queue matching to solve the task scheduling problem in a distributed computing environment. Finally, this paper constructs an improved algorithmic cognitive system architecture, and verifies the performance of the system through experimental research. The research results show that the system constructed in this paper is highly reliable.