On Information Coverage for Location Category Based Point-of-Interest Recommendation

Xuefeng Chen, Yifeng Zeng, Gao Cong, Shengchao Qin, Yanping Xiang, Yuanshun Dai

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Point-of-interest (POI) recommendation becomes a valuable service in location-based social networks. Based on the norm that similar users are likely to have similar preference of POIs, the current recommendation techniques mainly focus on users’ preference to provide accurate recommendation results. This tends to generate a list of homogeneous POIs that are clustered into a narrow band of location categories (like food, museum, etc.) in a city. However, users are more interested to taste a wide range of flavors that are exposed in a global set of location categories in the city. In this paper, we formulate a new POI recommendation problem, namely top-K location category based POI recommendation, by introducing information coverage to encode the location categories of POIs in a city. The problem is NP-hard. We develop a greedy algorithm and further optimization to solve this challenging problem. The experimental results on two real-world datasets demonstrate the utility of new POI recommendations and the superior performance of the proposed algorithms.
Original languageEnglish
Publication statusPublished - 25 Jan 2015
Event29th AAAI Conference on Artificial Intelligence - Hyatt Regency, Austin, United States
Duration: 25 Jan 201529 Jan 2015


Conference29th AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI 15
Country/TerritoryUnited States
Internet address

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Author(s) may reproduce, or have reproduced, their article/paper. Copyright © 2016 Association for the Advancement of Artificial Intelligence. All Rights Reserved. For full details see http://www.aaai.org/ocs/index.php/AAAI/AAAI15/about/submissions#copyrightNotice [Accessed: 22/12/2015]


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