Abstract
Location-based recommender systems have gained a lot of attention in both commercial domains and research communities where there are various approaches that have shown great potential for further studies. However, there has been little attention in previous research on location-based recommender systems for generating recommendations considering the locations of target users. Such recommender systems sometimes recommend places that are far from the target user’s current location. In this paper, we explore the issues of generating location recommendations for users who are traveling overseas by taking into account the user’s social influence and also the native or local expert’s knowledge. Accordingly, we have proposed a collaborative filtering recommendation framework called the Friend-And-Native-Aware Approach for Collaborative Filtering (FANA-CF), to generate reasonable location recommendations for users. We have validated our approach by systematic and extensive experiments using real-world datasets collected from Foursquare TM. By comparing algorithms such as the collaborative filtering approach (item-based collaborative filtering and user-based collaborative filtering) and the personalized mean approach, we have shown that our proposed approach has slightly outperformed the conventional collaborative filtering approach and personalized mean approach.
Highlights
Ever since traveling has become more accessible, people have been relying a lot on the location-based recommender system for places to visit during their voyage
By comparing algorithms such as the collaborative filtering approach and the personalized mean approach, we have shown that our proposed approach has slightly outperformed the conventional collaborative filtering approach and personalized mean approach
By exploring the strong social ties between friends and natives, we have proposed a location-based recommender system called Friend-And-Native-Aware Approach For Collaborative Filtering (FANA-collaborative filtering (CF)) for location-based recommendation based on collaborative ratings of commonly visited places made by social friends and the local experts of a certain range of geospatial location, and we perform extensive and systematic experimental analysis on FANA-CF
Summary
Ever since traveling has become more accessible, people have been relying a lot on the location-based recommender system for places to visit during their voyage. As recommendations are generated considering the locations visited by the similar users (who may have never been in Singapore), the recommended locations may be very far from the target user current location and may not be reachable by the target user at that particular moment. Those previous collaborative filtering approaches may recommend the same location, no matter where the user is currently located in the world. The systems with those approaches might fail to consider the social aspects that influence a user in the location-based recommender system
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