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Information 2017, 8(1), 20;

Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs

School of Management Science and Engineering, Shandong Normal University, Jinan 250014, China
Information Technology Bureau of Shandong Province, China Post Group, Jinan 250001, China
School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China
Author to whom correspondence should be addressed.
Academic Editor: Kurt Maly
Received: 12 December 2016 / Revised: 23 January 2017 / Accepted: 26 January 2017 / Published: 6 February 2017
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Point-of-interest (POI) recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user’s check-in behaviors from the history of what POIs she/he has visited, but never know how much she/he likes and why she/he does not like them. Recently, some researchers have noticed this problem and began to learn the user preferences from the partial order of POIs. However, these works give equal weight to each POI pair and cannot distinguish the contributions from different POI pairs. Intuitively, for the two POIs in a POI pair, the larger the frequency difference of being visited and the farther the geographical distance between them, the higher the contribution of this POI pair to the ranking function. Based on the above observations, we propose a weighted ranking method for POI recommendation. Specifically, we first introduce a Bayesian personalized ranking criterion designed for implicit feedback to POI recommendation. To fully utilize the partial order of POIs, we then treat the cost function in a weighted way, that is give each POI pair a different weight according to their frequency of being visited and the geographical distance between them. Data analysis and experimental results on two real-world datasets demonstrate the existence of user preference on different POI pairs and the effectiveness of our weighted ranking method. View Full-Text
Keywords: point-of-interest; location recommendation; LBSNs point-of-interest; location recommendation; LBSNs

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Guo, L.; Jiang, H.; Wang, X.; Liu, F. Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs. Information 2017, 8, 20.

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