Point of Interest Recommendation engine

Online Evaluation of Point-Of-Interest Recommendation Systems

In this work we describe a system to evaluate multiple pointof-interest recommendation systems. In this system each recommendation service will be exposed online and crowdsourced assessors will interact with merged results from multiple services, which are responding to suggestion requests live, in order to determine which system performs best. This work builds upon work done previously as part...

Learning recency based comparative choice towards point-of-interest recommendation

With the prevalence of GPS-enabled smart phones, Location Based Social Network (LBSN) has emerged and become a hot research topic during the past few years. As one of the most important components in LBSN, Points-of-Interests (POIs) has been extensively studied by both academia and industry, yielding POI recommendations to enhance user experience in exploring the city. In conventional methods, ...

Point-of-Interest Recommendation Using Heterogeneous Link Prediction

Venue recommendation in location-based social networks is among the more important tasks that enhances user participation on the social network. Despite its importance, earlier research have shown that the accurate recommendation of appropriate venues for users is a difficult task specially given the highly sparse nature of user check-in information. In this paper, we show how a comprehensive s...

PGRank: Personalized Geographical Ranking for Point-of-Interest Recommendation

Point-of-interest (POI) recommendation has become more and more important, since it could discover user behavior pattern and find interesting venues for them. To address this problem, we propose a rank-based method, PGRank, which integrates user geographical preference and latent preference into Bayesian personalized ranking framework. The experimental results on a real dataset show its effective.

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