Abstract
Point-of-Interest (POI) recommendation is an important personalized service in location-based social network (LBSN) which has wide applications. Traditional Collaborative Filtering methods suffer from cold-start and data sparsity problem. They also ignore connections among users and lose the opportunity to provide more accurate and personalized recommendations. In this paper, we propose a hybrid approach which incorporates user preference, geographic influence and social trust into POI recommendation system. In contrast to other trust-aware recommendation works, our approach exploits distrust links and investigates their propagation effects. We use a modified normalized Jaccard coefficient to measure the trust and distrust score. Several series of experiments are conducted and the results show that our approach perform better than the traditional Collaborative Filtering in terms of accuracy and user satisfaction.
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Acknowledgment
This work was supported in part by the National Science Foundation of China (61100048), the Natural Science Foundation of Heilongjiang Province (F2016034), the Education Department of Heilongjiang Province (12531498).
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Zhu, J., Ming, Q., Liu, Y. (2018). Trust-Distrust-Aware Point-of-Interest Recommendation in Location-Based Social Network. In: Chellappan, S., Cheng, W., Li, W. (eds) Wireless Algorithms, Systems, and Applications. WASA 2018. Lecture Notes in Computer Science(), vol 10874. Springer, Cham. https://doi.org/10.1007/978-3-319-94268-1_58
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DOI: https://doi.org/10.1007/978-3-319-94268-1_58
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