This paper proposes a matrix factorization framework with social regularization, which can be easily extended to incorporate other contextual information, like social tags, etc, and demonstrates that the approaches outperform other state-of-the-art methods.
A factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems by employing both users' social network information and rating records is proposed.
This work proposes a novel probabilistic factor analysis framework, which naturally fuses the users' tastes and their trusted friends' favors together and coin the term Social Trust Ensemble to represent the formulation of the social trust restrictions on the recommender systems.
This paper is the first to fuse MF with geographical and social influence for POI recommendation in LBSNs by capturing the geographical influence via modeling the probability of a user’s check-in on a location as a Multi-center Gaussian Model (MGM).
This paper proposes 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, and shows that the algorithm achieves better prediction accuracy than other approaches.
This paper proposes a novel matrix factorization method, namely FPMC-LR, to embed the personalized Markov chains and the localized regions in the check-in sequence, and utilizes the information of localized regions to boost recommendation.
The comprehensive experimental analysis shows that WSRec achieves better prediction accuracy than other approaches, and includes a user-contribution mechanism for Web service QoS information collection and an effective and novel hybrid collaborative filtering algorithm for Web Service QoS value prediction.
This work introduces a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student's mastery level of each concept.
This work proposes a new model named Metapath Aggregated Graph Neural Network (MAGNN), which achieves more accurate prediction results than state-of-the-art baselines and employs three major components, i.e., the node content transformation to encapsulate input node attributes, the intra-metapath aggregation to incorporate intermediate semantic nodes, and the inter-metal aggregation to combine messages from multiple metapaths.
A model, SBPR (Social Bayesian Personalized Ranking), is developed based on the simple observation that users tend to assign higher ranks to items that their friends prefer, and it is shown that SBPR outperforms alternatives in ranking prediction both in warm- and cold-start settings.