Abstract
The pervasive use of Location-based Social Networks calls for more precise Point-of-Interest recommendation. The probability of a user’s visit to a target place is influenced by multiple factors. Though there are several fusion models in such fields, heterogeneous information are not considered comprehensively. To this end, we propose a novel probabilistic latent factor model by jointly considering the social correlation, geographical influence and users’ preference. To be specific, a variant of Latent Dirichlet Allocation is leveraged to extract the topics of both user and POI from reviews which is denoted as explicit interest. Then, Probabilistic Latent Factor Model is introduced to depict the implicit interest. Moreover, Kernel Density Estimation and friend-based Collaborative Filtering are leveraged to model user’s geographic allocation and social correlation respectively. Thus, we propose CoSoLoRec, a fusion framework, to ameliorate the recommendation. Experiments on two real-word datasets show the superiority of our approach over the state-of-the-art methods.
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References
Tobler, W.R.: A computer movie simulating urban growth in the Detroit region. Econ. Geogr. 46, 234–240 (1970)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Revi. Sociol. 27, 415–444 (2001)
Dehnad, K.: Density estimation for statistics and data analysis. Technometrics 29(4), 495–495 (1987)
Ye, M., et al.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of ACM SIGIR. ACM (2011)
Cheng, C., Yang, H., et al.: Fused matrix factorization with geographical and social influence in location-based social networks. In: AAAI (2012)
Hu, B., Ester, M.: Spatial topic modeling in online social media for location recommendation. In: Proceedings of the 7th ACM Recsys. ACM (2013)
Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: NIPS (2007)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Liu, B., Xiong, H.: Point-of-interest recommendation in location based social networks with topic and location awareness. In: SDM, vol. 13 (2013)
Liu, B., et al.: Learning geographical preferences for point-of-interest recommendation. In: Proceedings of ACM SIGKDD. ACM (2013)
Liu, B., et al.: A general geographical probabilistic factor model for point of interest recommendation. TKDE 27, 1167–1179 (2015)
Ma, H., Lyu, M.R., King, I.: Learning to recommend with trust and distrust relationships. In: Proceedings of ACM Recsys. ACM (2009)
Tong, H., Faloutsos, C., Pan, J.-Y.: Fast random walk with restart and its applications. In: Proceedings of IEEE ICDM. IEEE Computer Society (2006)
Ma, H., et al.: Probabilistic factor models for web site recommendation. In: Proceedings of ACM SIGIR. ACM (2011)
Chen, Y., et al.: Factor modeling for advertisement targeting. In: NIPS (2009)
Anderson, M., et al.: Learning from the crowd: regression discontinuity estimates of the effects of an online review database. Econ. J. 122(563), 957–989 (2012)
Zhang, J., Chow, C.-Y., et al.: iGeoRec: a personalized and efficient geographical location recommendation framework. IEEE Trans. Serv. Comput. 8, 701–714 (2015)
Schmidt, M.N., Winther, O., Hansen, L.K.: Bayesian non-negative matrix factorization. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds.) ICA 2009. LNCS, vol. 5441, pp. 540–547. Springer, Heidelberg (2009). doi:10.1007/978-3-642-00599-2_68
Kurashima, T., Iwata, T., Hoshide, T., Takaya, N., Fujimura, K.: Geo topic model: joint modeling of user’s activity area and interests for location recommendation. In: Proceedings of ACM WSDM. ACM (2013)
Yin, H., Sun, Y., Cui, B., Hu, Z., Chen, L.: Lcars: a location-content-aware recommender system. In: Proceedings of ACM SIGKDD. ACM (2013)
Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., Rui, Y.: GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: Proceedings of ACM SIGKDD. ACM (2014)
Zheng, N., Jin, X., Li, L.: Cross-region collaborative filtering for new point-of-interest recommendation. In: Proceedings of WWW Companion (2013)
Zhang, C., Wang, K.: POI recommendation through cross-region collaborative filtering. KIS 46, 369–387 (2016)
Ye, M., Shou, D., Lee, W.-C., et al.: On the semantic annotation of places in location-based social networks. In: Proceedings of ACM SIGKDD. ACM (2011)
Yin, P., Luo, P., Lee, W.-C., Wang, M.: App recommendation: a contest between satisfaction and temptation. In: Proceedings of ACM WSDM. ACM (2013)
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Guo, H., Li, X., He, M., Zhao, X., Liu, G., Xu, G. (2016). CoSoLoRec: Joint Factor Model with Content, Social, Location for Heterogeneous Point-of-Interest Recommendation. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_48
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DOI: https://doi.org/10.1007/978-3-319-47650-6_48
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