Abstract
Point of interest (POI) recommendation on Location Based Social Networks (LBSN) is challenging as the data available for predicting the next point of interest is highly sparse. Addressing the sparsity issue becomes one of the keys to achieve accurate POI recommendation. A promising approach is to explore various types of relevant information carried by the network, e.g, network structures, spatial-temporal information and relations. In this paper, we put forward a hypergraph model to incorporate the higher-order relations of LBSNs for POI recommendation. Accordingly, we propose a hypergraph random walk (HRW) to be applied to such a complex hypergraph. The steady state distribution gives our derived recommendation on venues for each user. Experiments based on a real data set collected from Foursquare have been conducted to evaluate the efficiency and effectiveness of our proposed model with promising results obtained.
X. Li—The work of Xin Li is partially supported by National Program on Key Basic Research Project under Grant No. 2013CB329605 and NSFC under Grant No. 61300178.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)
Avin, C., Lando, Y., Lotker, Z.: Radio cover time in hyper-graphs. In: Proceedings of the 6th International Workshop on Foundations of Mobile Computing, pp. 3–12. ACM (2010)
Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the fourth ACM International Conference on Web Search and Data Mining, pp. 635–644. ACM (2011)
Bao, J., Zheng, Y., Mokbel, M.F.: Location-based and preference-aware recommendation using sparse geo-social networking data. Gis, pp. 199–208 (2012)
Alejandro, B.: Using graph partitioning techniques for neighbour selection in user-based collaborative filtering. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys 2012, pp. 213–216. ACM (2012)
Chen, C., Wang, C., Zhang, L., He, X., Bu, J., Tan, S.: Music recommendation by unified hypergraph: combining social media information and music content. In: Proceedings of the 18th Annual ACM International Conference on Multimedia, pp. 391–400 (2010)
Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: NIPS (2007)
Mnih, A., Salakhutdinov, R.: Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008 (2008)
Koren, C.V.Y., Bell, R.: Matrix factorization techniques for recommender systems. IEEE Computer 42(8), 30–37 (2009)
Xie, X., Ma, W-Y., Zheng, Y., Zhang, L.: Mining interesting locations and travel sequences from GPS trajectories. In: Proc. of 2009 Int. World Wide Web Conf. (WWW 2009), pp. 791–800 (2009)
Zhou, D., Huang, J., Schölkopf, B.: Learning with hypergraphs: clustering, classification, and embedding. In: Advances in Neural Information Processing Systems, pp. 1601–1608 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Qi, M., Li, X., Liao, L., Song, D., Cheung, W.K. (2015). Deriving an Effective Hypergraph Model for Point of Interest Recommendation. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_71
Download citation
DOI: https://doi.org/10.1007/978-3-319-25159-2_71
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25158-5
Online ISBN: 978-3-319-25159-2
eBook Packages: Computer ScienceComputer Science (R0)