Where you like to go next: Successive point-of-interest recommendation
Refereed conference paper presented and published in conference proceedings


其它資訊
摘要Personalized point-of-interest (POI) recommendation is a significant task in location-based social networks (LBSNs) as it can help provide better user experience as well as enable third-party services, e.g., launching advertisements. To provide a good recommendation, various research has been conducted in the literature. However, pervious efforts mainly consider the "check-ins" in a whole and omit their temporal relation. They can only recommend POI globally and cannot know where a user would like to go tomorrow or in the next few days. In this paper, we consider the task of successive personalized POI recommendation in LBSNs, which is a much harder task than standard personalized POI recommendation or prediction. To solve this task, we observe two prominent properties in the check-in sequence: personalized Markov chain and region localization. Hence, we propose a novel matrix factorization method, namely FPMCLR, to embed the personalized Markov chains and the localized regions. Our proposed FPMC-LR not only exploits the personalized Markov chain in the check-in sequence, but also takes into account users' movement constraint, i.e., moving around a localized region. More importantly, utilizing the information of localized regions, we not only reduce the computation cost largely, but also discard the noisy information to boost recommendation. Results on two real-world LBSNs datasets demonstrate the merits of our proposed FPMC-LR.
著者Cheng C., Yang H., Lyu M.R., King I.
會議名稱23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
會議開始日03.08.2013
會議完結日09.08.2013
會議地點Beijing
會議國家/地區中國
會議論文集題名Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence
出版年份2013
月份12
日期1
頁次2605 - 2611
國際標準書號9781577356332
國際標準期刊號1045-0823
語言英式英語

上次更新時間 2020-05-09 於 02:00