Exploiting the roles of aspects in personalized POI recommender systems

Article

Abstract

The evolution of World Wide Web (WWW) and the smart-phone technologies have revolutionized our daily life. This has facilitated the emergence of many useful systems, such as Location-based Social Networks (LBSN) which have provisioned many factors that are crucial for selection of Point-of-Interests (POI). Some of the major factors are: (i) the location attributes, such as geo-coordinates, category, and check-in time, (ii) the user attributes, such as, comments, tips, reviews, and ratings made to the locations, and (iii) other information, such as the distance of the POI from user’s house/office, social tie between users, and so forth. Careful selection of such factors can have significant impact on the efficiency of POI recommendation. In this paper, we define and analyze the fusion of different major aspects in POI recommendation. Such a fusion and analysis is barely explored by other researchers. The major contributions of this paper are: (i) it analyzes the role of different aspects (e.g., check-in frequency, social, temporal, spatial, and categorical) in the location recommendation, (ii) it proposes two fused models—a ranking-based, and a matrix factorization-based, that incorporate all the major aspects into a single recommendation model, and (iii) it evaluates the proposed models against two real-world datasets.

Keywords

Information retrieval Context aware recommendation POI recommendation Social networks 

References

  1. Bao J, Zheng Y, Mokbel MF (2012) Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of the 20th international conference on advances in geographic information systems. ACM, pp 199–208Google Scholar
  2. Baral R, Li T (2016) Maps: a multi aspect personalized poi recommender system. In: Proceedings of the 10th ACM conference on recommender systems. ACM, pp 281–284Google Scholar
  3. Baral R, Wang D, Li T, Chen SC (2016) Geotecs: exploiting geographical, temporal, categorical and social aspects for personalized poi recommendation. In: 2016 IEEE 17th international conference on information reuse and integration (IRI). IEEE, pp 94–101Google Scholar
  4. Boutsidis C, Gallopoulos E (2008) SVD based initialization: a head start for nonnegative matrix factorization. Pattern Recognit 41(4):1350–1362CrossRefMATHGoogle Scholar
  5. Brin S, Motwani R, Page L, Winograd T (1998) What can you do with a web in your pocket? IEEE Data Eng Bull 21(2):37–47Google Scholar
  6. Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: successive point-of-interest recommendation. In: IJCAI, vol 13, pp 2605–2611Google Scholar
  7. Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 1082–1090Google Scholar
  8. Gao H, Tang J, Liu H (2012) Exploring social-historical ties on location-based social networks. In: ICWSMGoogle Scholar
  9. Gao H, Tang J, Hu X, Liu H (2013) Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of the 7th ACM conference on recommender systems. ACM, pp 93–100Google Scholar
  10. Gibson D, Kleinberg J, Raghavan P (1998) Inferring web communities from link topology. In: Proceedings of the ninth ACM conference on hypertext and hypermedia: links, objects, time and space—structure in hypermedia systems: links, objects, time and space—structure in hypermedia systems. ACM, pp 225–234Google Scholar
  11. Haveliwala TH (2002) Topic-sensitive pagerank. In: Proceedings of the 11th international conference on World Wide Web. ACM, pp 517–526Google Scholar
  12. Hu B, Ester M (2013) Spatial topic modeling in online social media for location recommendation. In: Proceedings of the 7th ACM conference on recommender systems. ACM, pp 25–32Google Scholar
  13. Hu L, Sun A, Liu Y (2014) Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction. In: Proceedings of the 37th international ACM SIGIR conference on research & development in information retrieval. ACM, pp 345–354Google Scholar
  14. Jin Z, Shi D, Wu Q, Yan H, Fan H (2012) LBSNRank: personalized pagerank on location-based social networks. In: Proceedings of the 2012 ACM conference on ubiquitous computing. ACM, pp 980–987Google Scholar
  15. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42:30–37CrossRefGoogle Scholar
  16. Levandoski JJ, Sarwat M, Eldawy A, Mokbel MF (2012) Lars: a location-aware recommender system. In: 2012 IEEE 28th international conference on data engineering (ICDE). IEEE, pp 450–461Google Scholar
  17. Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y (2014) GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 831–840Google Scholar
  18. Liu B, Xiong H (2013) Point-of-interest recommendation in location based social networks with topic and location awareness. In: Proceedings of the 2013 SIAM international conference on data mining. SIAM, pp 396–404Google Scholar
  19. Liu B, Fu Y, Yao Z, Xiong H (2013a) Learning geographical preferences for point-of-interest recommendation. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1043–1051Google Scholar
  20. Liu X, Liu Y, Aberer K, Miao C (2013b) Personalized point-of-interest recommendation by mining users’ preference transition. In: Proceedings of the 22nd ACM international conference on conference on information & knowledge management. ACM, pp 733–738Google Scholar
  21. Liu B, Xiong H, Papadimitriou S, Fu Y, Yao Z (2015) A general geographical probabilistic factor model for point of interest recommendation. IEEE Trans Knowl Data Eng 27(5):1167–1179CrossRefGoogle Scholar
  22. Liu Y, Liu C, Liu B, Qu M, Xiong H (2016) Unified point-of-interest recommendation with temporal interval assessment. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1015–1024Google Scholar
  23. Page L, Brin S, Motwani R, Winograd T (1999) The PageRank citation ranking: bringing order to the web. Technical report, Stanford InfoLabGoogle Scholar
  24. Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24(5):513–523CrossRefGoogle Scholar
  25. Stepan T, Morawski JM, Dick S, Miller J (2016) Incorporating spatial, temporal, and social context in recommendations for location-based social networks. IEEE Trans Comput Soc Syst 3(4):164–175CrossRefGoogle Scholar
  26. Tobler WR (1970) A computer movie simulating urban growth in the detroit region. Econ Geog 46:234–240CrossRefGoogle Scholar
  27. Wang H, Terrovitis M, Mamoulis N (2013) Location recommendation in location-based social networks using user check-in data. In: Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 374–383Google Scholar
  28. Wang W, Yin H, Chen L, Sun Y, Sadiq S, Zhou X (2015) Geo-sage: a geographical sparse additive generative model for spatial item recommendation. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1255–1264Google Scholar
  29. Wang S, Wang Y, Tang J, Shu K, Ranganath S, Liu H (2017) What your images reveal: exploiting visual contents for point-of-interest recommendation. In: Proceedings of the 26th international conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp 391–400Google Scholar
  30. Wu HC, Luk RWP, Wong KF, Kwok KL (2008) Interpreting tf-idf term weights as making relevance decisions. ACM Trans Inf Syst (TOIS) 26(3):13CrossRefGoogle Scholar
  31. Xie M, Yin H, Wang H, Xu F, Chen W, Wang S (2016) Learning graph-based POI embedding for location-based recommendation. In: Proceedings of the 25th ACM international on conference on information and knowledge management. ACM, pp 15–24Google Scholar
  32. Yang D, Zhang D, Yu Z, Wang Z (2013) A sentiment-enhanced personalized location recommendation system. In: Proceedings of the 24th ACM conference on hypertext and social media. ACM, pp 119–128Google Scholar
  33. Ye M, Yin P, Lee WC (2010) Location recommendation for location-based social networks. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 458–461Google Scholar
  34. Ye M, Yin P, Lee WC, Lee DL (2011) 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. ACM, pp 325–334Google Scholar
  35. Yin H, Sun Y, Cui B, Hu Z, Chen L (2013) Lcars: a location-content-aware recommender system. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 221–229Google Scholar
  36. Yin H, Zhou X, Shao Y, Wang H, Sadiq S (2015) Joint modeling of user check-in behaviors for point-of-interest recommendation. In: Proceedings of the 24th ACM international on conference on information and knowledge management. ACM, pp 1631–1640Google Scholar
  37. Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Time-aware point-of-interest recommendation. In: Proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 363–372Google Scholar
  38. Zhang JD, Chow CY (2015) GeoSoCa: exploiting geographical, social and categorical correlations for point-of-interest recommendations. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 443–452Google Scholar
  39. Zheng Y, Chen Y, Xie X, Ma WY (2009) GeoLife2. 0: a location-based social networking service. In: Tenth international conference on mobile data management: systems, services and middleware, 2009. MDM’09. IEEE, pp 357–358Google Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.School of Computing and Information SciencesFlorida International UniversityMiamiUSA

Personalised recommendations