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Personalized location recommendation by aggregating multiple recommenders in diversity

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Abstract

Location recommendation is an important feature of social network applications and location-based services. Most existing studies focus on developing one single method or model for all users. By analyzing data from two real location-based social networks (Foursquare and Gowalla), in this paper we reveal that the decisions of users on place visits depend on multiple factors, and different users may be affected differently by these factors. We design a location recommendation framework that combines results from various recommenders that consider different factors. Our framework estimates, for each individual user, the underlying influence of each factor to her. Based on the estimation, we aggregate suggestions from different recommenders to derive personalized recommendations. Experiments on Foursquare and Gowalla show that our proposed method outperforms the state-of-the-art methods on location recommendation.

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Notes

  1. https://developer.foursquare.com/.

  2. Cosine similarity and Pearson’s correlation coefficient have very close performance in our experiments, thus we just use cosine similarity for simplicity.

  3. Note that sometimes a recommender may fail to generate a list of length 10. For example, FCF (R 2) requires that the target user has some friends but loners do exist in LBSNs. In such cases, we complement the length-10 list with the most popular locations.

  4. It is worth mentioning that a trivial implementation of LURWA is to put equal weights on component recommenders. This, however, usually leads to very bad recommendations due to the diversities we studied in Section 2 (Figs. 1–2). Indeed, the essence of learning is to identify those good recommenders out of a large population of bad ones, with regard to some individual user.

  5. POI recommendation on sparse datasets has relatively low precision and recall values [15, 42]. We focus on comparing methods’ relative performance, instead of the absolute performances.

References

  1. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. TKDE 17(6):734–749

    Google Scholar 

  2. Bao J, Zheng Y, Mokbel MF (2012) Location-based and preference-aware recommendation using sparse geo-social networking data SIGSPATIAL GIS

    Google Scholar 

  3. Bar A, Rokach L, Shani G, Shapira B, Schclar A (2013) Improving simple collaborative filtering models using ensemble methods MCS

    Google Scholar 

  4. Burges C, Ragno R, Le QV (2007) Learning to rank with nonsmooth cost functions NIPS

    Google Scholar 

  5. Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2005) Learning to rank using gradient descent ICML

    Google Scholar 

  6. Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks KDD

    Google Scholar 

  7. Ding Y, Li X (2005) Time weight collaborative filtering CIKM

    Google Scholar 

  8. Dwork C, Kumar R, Naor M, Sivakumar D (2001) Rank aggregation methods for the web WWW

    Google Scholar 

  9. Freund Y, Iyer R, Schapire RE, Singer Y (2003) An efficient boosting algorithm for combining preferences. JMLR 4:933–969

    Google Scholar 

  10. Gao H, Tang J, Hu X, Liu H (2013) Exploring temporal effects for location recommendation on location-based social networks Recsys

    Google Scholar 

  11. Gao H, Tang J, Liu H (2012) gSCorr: modeling geo-social correlations for new check-ins on location-based social networks CIKM

    Google Scholar 

  12. Gao HG, Tang J, Hu X, Liu H (2015) Content-aware point of interest recommendation on location-based social networks AAAI

    Google Scholar 

  13. Griesner JB, Abdessalem T, Naacke H (2015) Poi recommendation: towards fused matrix factorization with geographical and temporal influences Proceedings of the 9th ACM conference on recommender systems, RecSys ’15

    Google Scholar 

  14. Halvey M, Punitha P, Hannah D, Villa R, Hopfgartner F, Goyal A, Jose JM (2009) Diversity, assortment, dissimilarity, variety: a study of diversity measures using low level features for video retrieval ECIR

    Google Scholar 

  15. Hu B, Jamali M, Ester M (2013) Spatio-temporal topic modeling in mobile social media for location recommendation 2013 IEEE 13th international conference on data mining, dallas, TX, USA, December 7–10, 2013, pp 1073–1078

    Google Scholar 

  16. Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets ICDM

    Google Scholar 

  17. Jahrer M, Töscher A, Legenstein R (2010) Combining predictions for accurate recommender systems KDD

    Google Scholar 

  18. Jamali M, Ester M (2009) Trustwalker: a random walk model for combining trust-based and item-based recommendation Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’09

    Google Scholar 

  19. Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of ir techniques. ACM Trans Inf Syst 20(4):422–446

    Article  Google Scholar 

  20. Joachims T (2002) Optimizing search engines using clickthrough data KDD

    Google Scholar 

  21. Konstas I, Stathopoulos V, Jose JM (2009) On social networks and collaborative recommendation SIGIR

    Google Scholar 

  22. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model KDD

    Google Scholar 

  23. Leung KWT, Lee DL, Lee WC (2011) CLR: a collaborative location recommendation framework based on co-clustering SIGIR

    Google Scholar 

  24. Li N, Yu Y, Zhou ZH (2012) Diversity regularized ensemble pruning Proceedings of the 2012 european conference on machine learning and knowledge discovery in databases - Volume Part I, ECML PKDD’12, pp 330–345

    Google Scholar 

  25. Li X, Cong G, Li XL, Pham TAN, Krishnaswamy S (2015) Rank-geofm: a ranking based geographical factorization method for point of interest recommendation Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, SIGIR ’15, pp 433–442

    Chapter  Google Scholar 

  26. 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 KDD

    Google Scholar 

  27. Liu B, Fu Y, Yao Z, Xiong H (2013) Learning geographical preferences for point-of-interest recommendation KDD

    Google Scholar 

  28. Liu X, Liu Y, Aberer K, Miao C (2013) Personalized point-of-interest recommendation by mining users’ preference transition CIKM

    Google Scholar 

  29. Lu Z, Wang H, Mamoulis N, Tu W, Cheung DW (2015) Personalized location recommendation by aggregating multiple recommenders in diversity Proceedings of the workshop on location-aware recommendations, localrec 2015, co-located with the 9th ACM conference on recommender systems (RecSys 2015)

    Google Scholar 

  30. Pan R, Zhou Y, Cao B, Liu NN, Lukose R, Scholz M, Yang Q (2008) One-class collaborative filtering Proceedings of the 2008 eighth IEEE international conference on data mining, ICDM ’08

    Google Scholar 

  31. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: Bayesian personalized ranking from implicit feedback UAI

    Google Scholar 

  32. Sang J, Mei T, Sun JT, Xu C, Li S (2012) Probabilistic sequential pois recommendation via check-in data SIGSPATIAL GIS

    Google Scholar 

  33. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms WWW

    Google Scholar 

  34. Schclar A, Tsikinovsky A, Rokach L, Meisels A, Antwarg L (2009) Ensemble methods for improving the performance of neighborhood-based collaborative filtering RecSys

    Google Scholar 

  35. Song Y, Wang H, He X (2014) Adapting deep RankNet for personalized search WSDM

    Google Scholar 

  36. Tang L, Jiang Y, Li L, Li T (2014) Ensemble contextual bandits for personalized recommendation Recsys

    Google Scholar 

  37. Tiemann M, Pauws S (2007) Towards ensemble learning for hybrid music recommendation RecSys

    Google Scholar 

  38. Wang H, He X, Chang MW, Song Y, White RW, Chu W (2013) Personalized ranking model adaptation for web search SIGIR

    Google Scholar 

  39. Wang H, Terrovitis M, Mamoulis N (2013) Location recommendation in location-based social networks using user check-in data Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems, SIGSPATIAL’13

    Google Scholar 

  40. Ye M, Yin P, Lee WC (2010) Location recommendation for location-based social networks SIGSPATIAL GIS

    Google Scholar 

  41. Ye M, Yin P, Lee WC, Lee DL (2011) Exploiting geographical influence for collaborative point-of-interest recommendation SIGIR

    Google Scholar 

  42. Yin H, Sun Y, Cui B, Hu Z, Chen L (2013) LCARS: a location-content-aware recommender system KDD

    Google Scholar 

  43. Yin H, Zhou X, Shao Y, Wang H, Sadiq S (2015) Joint modeling of user check-in behaviors for point-of-interest recommendation Proceedings of the 24th ACM international on conference on information and knowledge management, CIKM ’15

    Google Scholar 

  44. Ying JJC, Lu EHC, Kuo WN, Tseng VS (2012) Urban point-of-interest recommendation by mining user check-in behaviors UrbComp

    Google Scholar 

  45. Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Time-aware point-of-interest recommendation SIGIR

    Google Scholar 

  46. Zhang JD, Chow CY (2013) iGSLR: personalized geo-social location recommendation - a kernel density estimation approach SIGSPATIAL GIS

    Google Scholar 

  47. Zhang JD, Chow CY, Zheng Y (2015) Orec: an opinion-based point-of-interest recommendation framework Proceedings of the 24th ACM international on conference on information and knowledge management, CIKM ’15

    Google Scholar 

  48. Zhao T, McAuley JJ, King I (2014) Leveraging social connections to improve personalized ranking for collaborative filtering CIKM

    Google Scholar 

  49. Zheng VW, Zheng Y, Xie X, Yang Q (2010) Collaborative location and activity recommendations with GPS history data WWW

    Google Scholar 

  50. Zheng Y, Jestes J, Phillips JM, Li F (2013) Quality and efficiency for kernel density estimates in large data Proceedings of the 2013 ACM SIGMOD international conference on management of data, SIGMOD ’13

    Google Scholar 

  51. Zheng Y, Zhang L, Xie X, Ma WY (2009) Mining interesting locations and travel sequences from GPS trajectories WWW

    Google Scholar 

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Acknowledgements

This work was supported by grant GRF 17205015 from Hong Kong RGC, National NSF of China (No. 61432008, 61503178) and NSF of Jiangsu, China (No. BK20150587).

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Authors and Affiliations

  1. Department of Computer Science, The University of Hong Kong, Hong Kong, China

    Ziyu Lu, Nikos Mamoulis, Wenting Tu & David W. Cheung

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Hao Wang

Authors
  1. Ziyu Lu
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  2. Hao Wang
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  3. Nikos Mamoulis
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  4. Wenting Tu
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  5. David W. Cheung
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Correspondence to Hao Wang.

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Lu, Z., Wang, H., Mamoulis, N. et al. Personalized location recommendation by aggregating multiple recommenders in diversity. Geoinformatica 21, 459–484 (2017). https://doi.org/10.1007/s10707-017-0298-x

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  • DOI: https://doi.org/10.1007/s10707-017-0298-x

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