Integrating with Social Network to Enhance Recommender System Based-on Dempster-Shafer Theory

In this paper, we developed a new collaborative filtering recommender system integrating with a social network that contains all users. In this system, user preferences and community preferences extracted from the social network are modeled as mass functions, and Dempster’s rule of combination is selected for fusing the preferences. Especially, with the community preferences, both the sparsity and cold-start problems are completely eliminated. So as to evaluate and demonstrate the advantage of the new system, we have conducted a range of experiments using Flixster data set.

[1]  Hsinchun Chen,et al.  Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering , 2004, TOIS.

[2]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[3]  Jie Lu,et al.  A hybrid trust‐enhanced collaborative filtering recommendation approach for personalized government‐to‐business e‐services , 2011, Int. J. Intell. Syst..

[4]  Jie Lu,et al.  An effective recommender system by unifying user and item trust information for B2B applications , 2015, J. Comput. Syst. Sci..

[5]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[6]  Kamal Premaratne,et al.  CoFiDS: A Belief-Theoretic Approach for Automated Collaborative Filtering , 2011, IEEE Transactions on Knowledge and Data Engineering.

[7]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[8]  Hong Yan,et al.  Recommender systems based on social networks , 2015, J. Syst. Softw..

[9]  John Riedl,et al.  An Algorithmic Framework for Performing Collaborative Filtering , 1999, SIGIR Forum.

[10]  Van-Nam Huynh,et al.  A Community-Based Collaborative Filtering System Dealing with Sparsity Problem and Data Imperfections , 2014, PRICAI.

[11]  Jennifer Xu,et al.  Data Mining for Social Network Data , 2010, Annals of Information Systems.

[12]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[13]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[14]  Steve Gregory,et al.  A Fast Algorithm to Find Overlapping Communities in Networks , 2008, ECML/PKDD.

[15]  Ioannis Konstas,et al.  On social networks and collaborative recommendation , 2009, SIGIR.

[16]  María N. Moreno García,et al.  A hybrid recommendation approach for a tourism system , 2013, Expert Syst. Appl..

[17]  Mei-Ling Shyu,et al.  Rule Mining and Classification in a Situation Assessment Application: A Belief-Theoretic Approach for Handling Data Imperfections , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Van-Nam Huynh,et al.  A Reliably Weighted Collaborative Filtering System , 2015, ECSQARU.

[19]  Dimitris Plexousakis,et al.  Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences , 2005, iTrust.

[20]  Peng Gang Sun,et al.  A framework of mapping undirected to directed graphs for community detection , 2015, Inf. Sci..

[21]  Boleslaw K. Szymanski,et al.  Towards Linear Time Overlapping Community Detection in Social Networks , 2012, PAKDD.

[22]  Hao Wu,et al.  Collaborative Topic Regression with social trust ensemble for recommendation in social media systems , 2016, Knowl. Based Syst..

[23]  Wesley W. Chu,et al.  A Social Network-Based Recommender System (SNRS) , 2010, Data Mining for Social Network Data.

[24]  Huan Liu,et al.  Community Detection and Mining in Social Media , 2010, Community Detection and Mining in Social Media.

[25]  Philippe Smets,et al.  Practical Uses of Belief Functions , 1999, UAI.

[26]  Daniel Thalmann,et al.  Merging trust in collaborative filtering to alleviate data sparsity and cold start , 2014, Knowl. Based Syst..

[27]  Mouzhi Ge,et al.  Recommender Systems in Computer Science and Information Systems-a Landscape of Research , 2012 .

[28]  Dunja Mladenic,et al.  Data Sparsity Issues in the Collaborative Filtering Framework , 2005, WEBKDD.

[29]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[30]  Jae-Gil Lee,et al.  Community Detection in Multi-Layer Graphs: A Survey , 2015, SGMD.

[31]  Jie Lu,et al.  A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system , 2015, Decis. Support Syst..

[32]  Isabelle Bloch,et al.  Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account , 1996, Pattern Recognit. Lett..

[33]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.