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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References
Zhang, D.-C., Li, M., Wang, C.-D.: Point of interest recommendation with social and geographical influence. In: IEEE International Conference on Big Data (Big Data), pp. 1070–1075 (2016)
Cheng, C., Yang, H., King, I., Lyu, M.R.: Fused matrix factorization with geographical and social influence in location-based social networks. In: AAAI, vol. 12, p. 1 (2012)
Liu, Y., Wei, W., Sun, A., Miao, C.: Exploiting geographical neighborhood characteristics for location recommendation. In: CIKM, pp. 739–748 (2014)
Ye, M., Yin, P., Lee, W.-C.: Location recommendation for location-based social networks. In: SIGSPATIAL, pp. 458–461 (2010)
Konstas, I., Stathopoulos, V., Jose, J.M.: On social networks and collaborative recommendation. In: SIGIR, pp. 195–202 (2009)
Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287–296 (2011)
Wang, H., Terrovitis, M., Mamoulis, N.: Location recommendation in location-based social networks using user check-in data. In: SIGSPATIAL, pp. 374–383 (2013)
Ye, M., Yin, P., Lee, W.-C., Lee, D.-L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: SIGIR, pp. 325–334 (2011)
Li, H., Ge, Y., Zhu, H.: Point-of-interest recommendations: learning potential check-ins from friends. In: KDD, pp. 975–984 (2016)
Lian, D., Ge, Y., Zhang, F., Yuan, N.J., Xie, X., Zhou, T., Rui, Y.: Content-aware collaborative filtering for location recommendation based on human mobility data. In: ICDM, pp. 261–270 (2015)
Li, H., Hong, R., Zhu, S., Ge, Y.: Point-of-interest recommender systems: a separate-space perspective. In: ICDM, pp. 231–240 (2015)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM, pp. 263–272 (2008)
Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)
Liu, B., Fu, Y., Yao, Z., Xiong, H.: Learning geographical preferences for point-of-interest recommendation. In: KDD, pp. 1043–1051 (2013)
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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