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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhao, S., King, I., Lyu, M.R.: A survey of point-of-interest recommendation in location-based social networks. Springer, New York (2016)
Zhu, J., Liu, Y., Yin, X., et al.: A new structure hole-based algorithm for influence maximization in large online social networks. IEEE Access, 1 (2017)
He, Z., Cai, Z., Wang, X.: Modeling propagation dynamics and developing optimized countermeasures for rumor spreading in online social networks. In: Proceedings of International Conference on Distributed Computing Systems, pp. 205–214. IEEE (2015)
Han, M., Li, Y., Li, J., et al.: Maximizing influence in sensed heterogenous social network with privacy preservation. Int. J. Sens. Netw. 1(1), 1 (2017)
Li, J., Cai, Z., Yan, M., Li, Y.: Using crowdsourced data in location-based social networks to explore influence maximization. In: Proceedings of the 35th Annual IEEE International Conference on Computer Communications (INFOCOM 2016) (2016)
Li, J., Cai, Z., Wang, J., et al.: Truthful incentive mechanisms for geographical position conflicting mobile crowdsensing systems. IEEE Trans. Comput. Soc. Syst.
Han, M., Li, L., Xie, Y., et al.: Cognitive approach for location privacy protection. IEEE Access PP(99), 1 (2018)
Zhang, D., Xu, C.: A collaborative filtering recommendation system by unifying user similarity and item similarity. In: Wang, L., Jiang, J., Lu, J., Hong, L., Liu, B. (eds.) WAIM 2011. LNCS, vol. 7142, pp. 175–184. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28635-3_17
Guo, G., Zhang, J., Thalmann, D.: Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowl. Based Syst. 57(2), 57–68 (2014)
Guo, G.: Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems. In: ACM Conference on Recommender Systems, pp. 451–454 (2013)
Ye, M., Yin, P., Lee, W.-C., Lee, D.-L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 325–334 (2011)
Wang, Y., Yin, G., Cai, Z., et al.: A trust-based probabilistic recommendation model for social networks. J. Netw. Comput. Appl. 55, 59–67 (2015)
Forsati, R., Mahdavi, M., Shamsfard, M., et al.: Matrix factorization with explicit trust and distrust side information for improved social recommendation. ACM Trans. Inf. Syst. 32(4), 17 (2014)
Lee, W.P., Ma, C.Y.: Enhancing collaborative recommendation performance by combining user preference and trust-distrust propagation in social networks. Knowl. Based Syst. 106, 125–134 (2016)
Gao, H., Tang, J., Hu, X., Liu, H.: Content-aware point of interest recommendation on location-based social networks. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 1721–1727. AAAI Press (2015)
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: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 831–840 (2014)
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).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-94268-1_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94267-4
Online ISBN: 978-3-319-94268-1
eBook Packages: Computer ScienceComputer Science (R0)