Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine

Kaihui Xu,Yi Shen,Dongjin Yu,Yihang Xu

doi:10.3390/ijgi10040258

Abstract

Point-Of-Interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the lack of negative samples and the complexities of check-in contexts limit their effectiveness significantly. This paper focuses on the problem of context-specific POI recommendation based on the check-in behaviors recorded by Location-Based Social Network (LBSN) services, which aims at recommending a list of POIs for a user to visit at a given context (such as time and weather). Specifically, a bidirectional influence correlativity metric is proposed to measure the semantic feature of user check-in behavior, and a contextual smoothing method to effectively alleviate the problem of data sparsity. In addition, the check-in probability is computed based on the geographical distance between the user’s home and the POI. Furthermore, to handle the problem of no negative feedback in LBSN, a weighted random sampling method is proposed based on contextual popularity. Finally, the recommendation results is obtained by utilizing Factorization Machine with Bayesian Personalized Ranking (BPR) loss. Experiments on a real dataset collected from Foursquare show that the proposed approach has better performance than others.

Highlights

With the rapid development and popularization of Internet technologies and mobile devices, Location-Based Social Networks (LBSNs), such as Foursquare and Yelp, have become increasingly popular
With the help of mobile devices, users can share their geographical locations in the LBSNs through “check-in” behaviors
(3) Since users prefer to visit nearby POIs, the check-in probability is computed based on the geographical distance between the user’s home and the POI. (4) To handle the problem of none negative feedback in LBSN, a weighted random sampling method is proposed based on contextual popularity

Summary

Introduction

With the rapid development and popularization of Internet technologies and mobile devices, Location-Based Social Networks (LBSNs), such as Foursquare and Yelp, have become increasingly popular. With the help of mobile devices, users can share their geographical locations in the LBSNs through “check-in” behaviors. The popularity of the LBSNs enables them to gather various types of information about users including users’. The personalized Point-Of-Interest (POI) recommendation service is designed to improve the LBSN service experience by mining user preferences through check-in data [1]. The key to effective POI recommendation is how to precisely model rich context information. Many factors exist that influence the place a user will visit. Users may have time-specific behaviors, which indicates the temporal factor [1]

Methods

Results

Conclusion