Evaluation of social, geography, location effects for point-of-interest recommendation

Nai Hung Cheng, Chia Hui Chang

研究成果: 會議貢獻類型會議論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

Recently, location-based social network services have become very popular. Therefore, point of interests (POIs) recommendation has also become a promising and hot research problem. In POIs recommendation, the number of locations could be more than the number of users, so it is a challenge to recommend relevant locations. In this paper, we incorporate user preference, social influence and attraction of locations in the recommendation. First, we use geographic influence for candidate selection. Furthermore, we propose a unified POI recommendation framework, which fuses user preference to a POIs with social influence and attraction of locations methods based on customized linear weighting. In addition, we discuss performance of classification-based models (logistic regression and libFM) for POI recommendation. Experimental results show the unified POI recommendation framework based on customized linear weighting outperforms other approaches.

原文???core.languages.en_GB???
頁面766-772
頁數7
DOIs
出版狀態已出版 - 2013
事件2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013 - Dallas, TX, United States
持續時間: 7 12月 201310 12月 2013

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???event.eventtypes.event.conference???2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013
國家/地區United States
城市Dallas, TX
期間7/12/1310/12/13

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