- (Chen et al., 2008) ⇒ Wen-Yen Chen, Dong Zhang, and Edward Y. Chang. (2008). “Combinational Collaborative Filtering for Personalized Community Recommendation.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401909
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 Expectation-Maximization 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.
|2008 CombinationalCollaborativeFilte||Wen-Yen Chen|
Edward Y. Chang
|Combinational Collaborative Filtering for Personalized Community Recommendation||KDD-2008 Proceedings||10.1145/1401890.1401909||2008|
|Author||Wen-Yen Chen +, Dong Zhang + and Edward Y. Chang +|
|journal||Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +|
|title||Combinational Collaborative Filtering for Personalized Community Recommendation +|