Combinational collaborative filtering for personalized community recommendation

Rapid growth in the amount of data available on social networking sites has made information retrieval increasingly challenging for users. In this paper, we propose a collaborative filtering method, Combinational Collaborative Filtering (CCF), to perform personalized community recommendations by considering multiple types of co-occurrences in social data at the same time. This filtering method fuses semantic and user information, then applies a hybrid training strategy that combines Gibbs sampling and ExpectationMaximization algorithm. To handle the large-scale dataset, parallel computing is used to speed up the model training. Through an empirical study on the Orkut dataset, we show CCF to be both effective and scalable. Categories and Subject Descriptors H.4.m [Information Systems Applications]: Miscellaneous General Terms Algorithms, Experimentation Keywords Collaborative filtering, probabilistic models, personalized recommendation

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: WenYen Chen, Dong Zhang, Edward Y. Chang
• Keywords: Collaborative Filtering Method, Combinational Collaborative, Data Mining, KDD 2008, Orkut Dataset