Aggregated Temporal Tensor Factorization Model for Point-of-interest Recommendation
Refereed conference paper presented and published in conference proceedings


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摘要Temporal influence plays an important role in a point-of-interest (POI) recommendation system that suggests POIs for users in location-based social networks (LBSNs). Previous studies observe that the user mobility in LBSNs exhibits distinct temporal features, summarized as periodicity, consecutiveness, and non-uniformness. By capturing the observed temporal features, a variety of systems are proposed to enhance POI recommendation. However, previous work does not model the three features together. More importantly, we observe that the temporal influence exists at different time scales, yet this observation cannot be modeled in prior work. In this paper, we propose an Aggregated Temporal Tensor Factorization (ATTF) model for POI recommendation to capture the three temporal features together, as well as at different time scales. Specifically, we employ temporal tensor factorization to model the check-in activity, subsuming the three temporal features together. Furthermore, we exploit a linear combination operator to aggregate temporal latent features’ contributions at different time scales. Experiments on two real life datasets show that the ATTF model achieves better performance than models capturing temporal influence at single scale. In addition, our proposed ATTF model outperforms the state-of-the-art methods.
著者Shenglin Zhao, Irwin King, Michael R. Lyu
會議名稱ICONIP 2016: International Conference on Neural Information Processing
會議開始日16.10.2016
會議完結日23.10.2016
會議地點Kyoto
會議國家/地區日本
會議論文集題名ICONIP 2016: Neural Information Processing
系列標題Lecture Notes in Computer Science
叢書冊次9949
出版年份2016
月份10
卷號9949
出版社Springer
頁次450 - 458
國際標準書號978-3-319-46674-3
國際標準期刊號0302-9743
語言美式英語

上次更新時間 2021-16-09 於 01:42