Combinational Collaborative Filtering for Personalized Community Recommendation
Venue
ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining (KDD), ACM (2008), pp. 115-123
Publication Year
2008
Authors
Wen-Yen Chen, Dong Zhang, Edward Chang
BibTeX
Abstract
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 ltering 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 ltering 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
