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Recommender systems with social regularization
In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. Expand
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SoRec: social recommendation using probabilistic matrix factorization
This paper proposes a factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems of recommender systems by employing both users' social network information and rating records. Expand
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Learning to recommend with social trust ensemble
We propose a novel probabilistic factor analysis framework for modeling recommender systems more accurately and realistically, which naturally fuses the users' tastes and their trusted friends' favors together. Expand
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Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks
We are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. Expand
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QoS-Aware Web Service Recommendation by Collaborative Filtering
We present a collaborative filtering approach for predicting QoS values of Web services and making Web service recommendation by taking advantages of past usage experiences of service users. Expand
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WSRec: A Collaborative Filtering Based Web Service Recommender System
We present WSRec, a Web service recommender system, which employs an effective and novel hybrid collaborative filtering algorithm for Web service QoS value prediction. Expand
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Where You Like to Go Next: Successive Point-of-Interest Recommendation
We propose a novel matrix factorization method, namely FPMC-LR, to embed the personalized Markov chains in the check-in sequence and the localized regions. Expand
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Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization
This paper proposes a collaborative quality-of-service (QoS) prediction approach for web services by taking advantage of the past web service usage experiences of service users. Expand
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Simple and Efficient Multiple Kernel Learning by Group Lasso
In this paper, we formulate a closed-form solution for optimizing the kernel weights based on the equivalence between group-lasso and MKL. Expand
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Investigating QoS of Real-World Web Services
We conduct several large-scale evaluations on real-world web services and collect comprehensive web service QoS data sets for validating various QoS-based approaches. Expand
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