A Bayesian Nonparametric Approach to Dynamic Dyadic Data Prediction
Refereed conference paper presented and published in conference proceedings

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其它資訊
摘要An important issue of using matrix factorization for recommender systems is to capture the dynamics of user preference over time for more accurate prediction. We find that considering the existence of clusters among users with respect to evolution behavior of their preference can improve performance effectively. This is especially important to commercial recommender systems, where the evolution of preference for different users is heterogeneous, and historical ratings are not enough to estimate the preference of each user individually. Based on this, we propose a novel Bayesian nonparametric method based on the Dirichlet process, to detect users sharing the same evolution behavior of their preference. For each community, we use vector autoregressive model~(VAR) to capture the evolution to explore higher-order dependency on historical user preference, and incorporate this feature with a novel adaptive prior strategy. We also derive variational inference approach to infer our method. Finally, we conduct extensive empirical experiments to show the advantage of our method over state-of-the-art algorithms.
著者ZHU Fengyuan, CHEN Guangyong, HENG Pheng Ann
會議名稱The IEEE International Conference on Data Mining series (ICDM)
會議開始日12.12.2016
會議完結日15.12.2016
會議地點Barcelona
會議國家/地區西班牙
會議論文集題名Data Mining (ICDM), 2016 IEEE 16th International Conference on
出版年份2016
出版社IEEE
國際標準書號978-1-5090-5474-9
電子國際標準書號978-1-5090-5473-2
電子國際標準期刊號2374-8486
語言美式英語

上次更新時間 2021-24-02 於 01:13