A Multi-factor Recommendation Algorithm for POI Recommendation

  • Rong YangEmail author
  • Xiaofeng Han
  • Xingzhong Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11242)


Point-of-Interest (POI) recommendation is an important service in Location-Based Social Networks (LBSNs). There are several approaches, such as collaborating filtering or content-based filtering, to solving the problem, but the quality of recommendation is low because of lack of personalized influencing factors for each user. In LBSNs, users’ history check-in data contain rich information, such as the geographic and textual information of the POIs, the time user visiting POIs, the friend relationship between users, etc. Recently, these factors are exploited to further improve the quality of recommendation. The major challenges are which factors to use and how to use them. In this paper, a multi-factor recommendation algorithm (MFRA) is proposed to address these challenges, which initially exploits the locality of user activities to create a candidate set of POIs, and for each candidate POI, the influence of friend relationship and the category and popularity of POI on each user are considered to improve the quality of recommendation. Experiments on the check-in datasets of Foursquare demonstrate a better precision and recall rate of the proposed algorithm.


POI recommendation Locality of POI Category of POI Popularity of POI Friend relationship 



This work is financially supported by the National Science and Technology Support Program of China (Grant No. 2015BAH37F01) and the CERNET Next Generation Internet Technology Innovation Project of China (Grant No. NGII20170518).


  1. 1.
    Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Min. Knowl. Discov. 5(1–2), 115–153 (2001)CrossRefGoogle Scholar
  2. 2.
    Yang, X., Guo, Y., Liu, Y., Steck, H.: A survey of collaborative filtering based social recommender systems. Comput. Commun. 41(5), 1–10 (2014)CrossRefGoogle Scholar
  3. 3.
    Wang, H., Terrovitis, M., Mamoulis, N.: Location recommendation in location based social networks using user check-in data. In: 21st ACM Sigspatial International Conference on Advances in Geographic Information Systems, pp. 374–383. ACM, Orlando (2013)Google Scholar
  4. 4.
    Liu, B., Fu, Y., Yao, Z., Xiong, H.: Learning geographical preferences for point of interest recommendation. In: 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1043–1051. ACM, Chicago (2013)Google Scholar
  5. 5.
    Bao, J., Zheng, Y., Mokbel, M.F.: Location-based and preference-aware recommendation using sparse geo-social networking data. In: 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 199–208. ACM, California (2012)Google Scholar
  6. 6.
    Gao, H., Tang, J., Hu, X.: Content-aware point of interest recommendation on location-based social networks. In: 29th AAAI Conference on Artificial Intelligence, pp. 1721–1727. AAAI, Texas (2015)Google Scholar
  7. 7.
    Schafer, J.B., Dan, F., Herlocker, J.: Collaborative filtering recommender systems. Adapt. Web 22(1), 291–324 (2007)CrossRefGoogle Scholar
  8. 8.
    Ye, M., Yin, P., Lee, W.C., Lee, D.L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 325–334. ACM, Beijing (2011)Google Scholar
  9. 9.
    Konstas, I., Stathopoulos, V., Jose, J.M.: On social networks and collaborative recommendation. In: 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 195–202. ACM, Boston (2009)Google Scholar
  10. 10.
    Yuan, Q., Cong, G., Ma, Z.: Time-aware point-of-interest recommendation. In: 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 363–372. ACM, Dublin (2013)Google Scholar
  11. 11.
    Cao, X., Dong, Y., Yang, P., Zhou, T., Liu, B.: POI recommendation based on meta-path in LBSN. Chin. J. Comput. 39(04), 675–684 (2016)MathSciNetGoogle Scholar
  12. 12.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook, 1st edn. Springer, New York (2011). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Taiyuan University of TechnologyJinzhongChina

Personalised recommendations