MARS: A multi-aspect recommender system for Point-of-Interest

Xin LI, Guandong XU, Enhong CHEN, Lin LI

Research output: Chapter in Book/Report/Conference proceedingChapters

10 Citations (Scopus)


With the pervasive use of GPS-enabled smart phones, location-based services, e.g., Location Based Social Networking (LBSN) have emerged. Point-of-Interests (POIs) Recommendation, as a typical component in LBSN, provides additional values to both customers and merchants in terms of user experience and business turnover. Existing POI recommendation systems mainly adopt Collaborative Filtering (CF), which only exploits user given ratings (i.e., user overall evaluation) about a merchant while regardless of the user preference difference across multiple aspects, which exists commonly in real scenarios. Meanwhile, besides ratings, most LBSNs also provide the review function to allow customers to give their opinions when dealing with merchants, which is often overlooked in these recommender systems. In this demo, we present MARS, a novel POI recommender system based on multi-aspect user preference learning from reviews by using utility theory. We first introduce the organization of our system, and then show how the user preferences across multiple aspects are integrated into our system alongside several case studies of mining user preference and POI recommendations. Copyright © 2015 IEEE.

Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE 31st International Conference on Data Engineering, ICDE 2015
Place of PublicationUSA
ISBN (Electronic)9781479979639
Publication statusPublished - 2015


Li, X., Xu, G., Chen, E., & Li, L. (2015). MARS: A multi-aspect recommender system for Point-of-Interest. In Proceedings of the 2015 IEEE 31st International Conference on Data Engineering, ICDE 2015 (pp. 1436-1439). IEEE.


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