Interdependent Model for Point-of-Interest Recommendation via Social Networks
Point-of-Interest (POI) recommendation is an important way to help people discover attractive places. POI recommendation approaches are usually based on collaborative filtering methods, whose performances are largely limited by the extreme scarcity of POI check-ins and a lack of rich contexts, and also by assuming the independence of locations. Recent strategies have been proposed to capture the relationship between locations based on statistical analysis, thereby estimating the similarity between locations purely based on the visiting frequencies of multiple users. However, implicit interactions with other link locations are overlooked, which leads to the discovery of incomplete information. This paper proposes a interdependent item-based model for POI recommender systems, which considers both the intra-similarity (i.e. co-occurrence of locations) and inter-similarity (i.e. dependency of locations via links) between locations, based on the TF-IDF conversion of check-in times. Geographic information, such as the longitude and latitude of locations, are incorporated into the interdependent model. Substantial experiments on three social network data sets verify the POI recommendation built with our proposed interdependent model achieves a significant performance improvement compared to the state-of-the-art techniques.
This work was supported by a Summer Scholarship project as well as two Griffith University’s 2018 New Researcher Grants, with Dr. Can Wang and Dr. Md Saiful Islam being Chief Investigators, respectively.
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