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Combinational collaborative filtering for personalized community recommendation

9 years 2 months ago
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
WenYen Chen, Dong Zhang, Edward Y. Chang
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2008
Where KDD
Authors WenYen Chen, Dong Zhang, Edward Y. Chang
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