document: 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 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 .
doc_bio_tags: O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O