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Personalized Point-of-Interest Recommendation by Mining Users' Preference Transition


Authors: X. Liu, Y. Liu, K. Aberer, and C. Miao
Title: Personalized Point-of-Interest Recommendation by Mining Users' Preference Transition
Abstract: Location-based social networks (LBSNs) offer researchers rich data to study people's online activities and mobility patterns. One important application of such studies is to provide personalized point-of-interest (POI) recommendations to enhance user experience in LBSNs. Previous solutions directly predict users' preference on locations but fail to provide insights about users' preference transitions among locations. In this work, we propose a novel category-aware POI recommendation model, which exploits the transition patterns of users' preference over location categories to improve location recommendation accuracy. Our approach consists of two stages: (1) preference transition (over location categories) prediction, and (2) category-aware POI recommendation. Matrix factorization is employed to predict a user's preference transitions over categories and then her preference on locations in the corresponding categories. Real data based experiments demonstrate that our approach outperforms the state-of-the-art POI recommendation models by at least 39.75% in terms of recall.
Keywords: Location-based social networks; Point-of-interest; Recommendation; User preference
Conference Name: 22nd ACM International Conference on Conference on Information and Knowledge Management (CIKM'13)
Location: San Francisco, USA
Publisher: ACM
Year: 2013
Accepted PDF File: Personalized_Point-of-Interest_Recommendation_by_Mining_Users_Preference_Transition_accepted.pdf
Permanent Link: http://dx.doi.org/10.1145/2505515.2505639
Reference: X. Liu, Y. Liu, K. Aberer, and C. Miao, “Personalized point-of-interest recommendation by mining users’ preference transition,” in Proceedings of the 22nd ACM International Conference on Conference on Information and Knowledge Management (CIKM’13). ACM, October–November 2013, pp. 733–738.
bibtex: 
@inproceedings{LILY-c11,
   author 	= {Liu, Xin and Liu, Yong and Aberer, Karl and Miao, Chunyan},
   title 	= {Personalized Point-of-Interest Recommendation by Mining Users' Preference Transition},
   booktitle 	= {Proceedings of the 22nd ACM International Conference on Conference on Information and Knowledge Management (CIKM'13)},
   year		= {2013},
   month	= {October--November}, 
   pages 	= {733-738},
   location 	= {San Francisco, USA},
   publisher 	= {ACM},
}