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

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 conte...

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Bibliographic Details
Published inISPRS international journal of geo-information Vol. 10; no. 4; p. 258
Main Authors Yu, Dongjin, Shen, Yi, Xu, Kaihui, Xu, Yihang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2021
Subjects
Algorithms
Bayesian analysis
Collaboration
Context
context-specific
Deep learning
Factorization
Factorization Machine
Feedback
heterogeneous information network
Influence
Location based services
location-based social network
Negative feedback
Neural networks
point-of-interest recommendation
Preferences
Principal components analysis
Probability theory
Random sampling
Sampling
Sampling methods
Social networks
Social organization
Sparsity
Statistical sampling
User behavior
Weather
weighted random sampling
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Summary: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.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi10040258