publication . Article . 2017

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

Lei Guo; Haoran Jiang; Xinhua Wang; Fangai Liu;
Open Access
  • Published: 01 Feb 2017 Journal: Information, volume 8, page 20 (eissn: 2078-2489, Copyright policy)
  • Publisher: MDPI AG
Abstract
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...
Subjects
free text keywords: Data mining, computer.software_genre, computer, Computer science, Point of interest, Artificial intelligence, business.industry, business, Know-how, Frequency difference, Machine learning, Ranking, Bayesian probability, Geographical distance, point-of-interest, location recommendation, LBSNs, Information technology, T58.5-58.64
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publication . Article . 2017

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

Lei Guo; Haoran Jiang; Xinhua Wang; Fangai Liu;