Abstract
The rapid development of Location-based Social Networks (LBSNs) has led to the great demand of personalized Point-of-interests (POIs) recommendation. Although previous researches have presented a variety of methods to recommend POIs by utilizing social relation, geographical mobility data and user content profile, they fail to address user/location’s cold-start problem with high-dimensional sparse data, and overlook the compatibility of social relation, content based methodology and collaborative filtering. To cope with these challenges, we analyze user’s check-in preference and find that it may be influenced in two spaces, namely Social Propagation Influence Space and Individual Attribute Influence Space. To this end, we propose a Social and Content based Collaborative Filtering Model (SCCF), which consists of a Social Relation Preference based Model (SRPB) considering social friends’ preference and a User Location Content-based Model (ULCB) matching the user attributes with location features. Extensive experiments on real-world datasets firmly demonstrate that the proposed SCCF model outperforms the state-of-the-art approaches while addressing cold-start problems in POI recommendation.
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Acknowledgment
This work was supported by the Fundamental Research Funds for the Central Universities (16lgzd15) and Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (No. 2016TQ03X542).
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Xu, YN., Xu, L., Huang, L., Wang, CD. (2017). Social and Content Based Collaborative Filtering for Point-of-Interest Recommendations. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_5
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DOI: https://doi.org/10.1007/978-3-319-70139-4_5
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