Point-of-interest recommendations via a supervised random walk algorithm

Guandong XU, Bin FU, Yanhui GU

Research output: Contribution to journalArticlespeer-review

28 Citations (Scopus)


Recently, location-based social networks (LBSNs) such as Foursquare and Whrrl have emerged as a new application for users to establish personal social networks and review various points of interest (POIs), triggering a new recommendation service aimed at helping users locate more preferred POIs. Although users' check-in activities could be explicitly considered as user ratings, in turn being utilized directly for collaborative filtering-based recommendations, such solutions don't differentiate the sentiment of reviews accompanying check-ins, resulting in unsatisfactory recommendations. This article proposes a new POI recommendation framework by simultaneously incorporating user check-ins and reviews along with side information into a tripartite graph and predicting personalized POI recommendations via a sentiment-supervised random walk algorithm. The experiments conducted on real data demonstrate the superiority of this approach in comparison with state-of-the-art techniques. Copyright © 2016 IEEE. All rights reserved.

Original languageEnglish
Pages (from-to)15-23
JournalIEEE Intelligent Systems
Issue number1
Publication statusPublished - 2016


Xu, G., Fu, B., & Gu, Y. (2016). Point-of-interest recommendations via a supervised random walk algorithm. IEEE Intelligent Systems, 31(1), 15-23. https://doi.org/10.1109/MIS.2016.4


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