Google Research

Combinational Collaborative Filtering for Personalized Community Recommendation

  • Wen-Yen Chen
  • Dong Zhang
  • Edward Chang
ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining (KDD), ACM (2008), pp. 115-123


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

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