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Aggregated Temporal Tensor Factorization Model for Point-of-Interest Recommendation

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Abstract

Point-of-interest (POI) recommendation is an important application in location-based social networks (LBSNs), which mines user check-in sequences to suggest interesting locations for users. Because user check-in behavior exhibits strong temporal patterns—for instance, users would like to check-in at restaurants at noon and visit bars at night. Hence, capturing the temporal influence is necessary to ensure the high performance in a POI recommendation system. Previous studies observe that the temporal characteristics of user mobility in LBSNs can be summarized in three aspects: periodicity, consecutiveness, and non-uniformness. However, previous work does not model the three characteristics together. More importantly, we observe that the temporal characteristics exist at different time scales, which cannot be modeled in prior work. In this paper, we propose an Aggregated Temporal Tensor Factorization (ATTF) model for POI recommendation to capture the three temporal features together, as well as at different time scales. Specifically, we employ a temporal tensor factorization method to model the check-in activity, subsuming the three temporal features together. Next, we exploit a linear combination operator to aggregate temporal latent features’ contributions at different time scales. Experiments on two real-world data sets show that the ATTF model achieves better performance than the state-of-the-art temporal models for POI recommendation.

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Notes

  1. We use cosine similarity here; other measures like Pearson correlation are also applicable.

References

  1. Cheng C, Yang H, King I, Lyu MR (2012) Fused matrix factorization with geographical and social influence in location-based social networks. In: Proceedings of the twenty-sixth AAAI conference on artificial intelligence. AAAI Press, pp 17–23

  2. Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: successive point-of-interest recommendation. In: Proceedings of the twenty-third international joint conference on artificial intelligence. AAAI Press, pp 2605–2611

  3. 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 1082–1090

  4. 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 93–100

  5. Gao H, Tang J, Hu X, Liu H (2015)Content-aware point of interest recommendation on location-based social networks. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence. AAAI Press, pp 1721–1727

  6. Gao H, Tang J, Liu H (2012) gscorr: modeling geo-social correlations for new check-ins on location-based social networks. In: Proceedings of the 21st ACM international conference on information and knowledge management. ACM, pp 1582–1586

  7. Grover A, Leskovec J(2016) node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 855–864

  8. Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: 2008 eighth IEEE international conference on data mining. Ieee, pp 263–272

  9. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  10. Le QV, Mikolov T (2014) Distributed representations of sentences and documents. In: Proceedings of the 31th international conference on machine learning, vol 14, pp 1188–1196

  11. Levy O, Goldberg Y (2014) Neural word embedding as implicit matrix factorization. In: Proceedings of the 27th international conference on neural information processing systems. MIT Press, pp 2177–2185

  12. Li Y, Xu L, Tian F, Jiang L, Zhong X, Chen E (2015) Word embedding revisited: a new representation learning and explicit matrix factorization perspective. In: Proceedings of the 25th international joint conference on artificial intelligence, pp 3650–3656

  13. Lian D, Ge Y, Zhang F, Yuan NJ, Xie X, Zhou T, Rui Y (2015) Content-aware collaborative filtering for location recommendation based on human mobility data. In: 2015 IEEE international conference on data mining (ICDM). IEEE, pp 261–270

  14. 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: ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 831–840

  15. Liu P, Qiu X, Huang X (2015) Learning context-sensitive word embeddings with neural tensor skip-gram model. In: Proceedings of the 25th international joint conference on artificial intelligence

  16. Liu X, Liu Y, Aberer K, Miao C (2013) Personalized point-of-interest recommendation by mining users’ preference transition. In: Proceedings of the 22nd ACM international conference on information & knowledge management. ACM, pp 733–738

  17. Liu Y, Liu Z, Chua TS, Sun M (2015) Topical word embeddings. In: Proceedings of the 29th AAAI conference on artificial intelligence

  18. Liu Y, Wei W, Sun A, Miao C (2014) Exploiting geographical neighborhood characteristics for location recommendation. In: ACM international conference on conference on information and knowledge management, pp 739–748

  19. Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: Proceedings of the fourth ACM international conference on web search and data mining. ACM, pp 287–296

  20. Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst 26:3111–3119

    Google Scholar 

  21. Pan R, Zhou Y, Cao B, Liu NN, Lukose R, Scholz M, Yang Q (2008) One-class collaborative filtering. In: Proceedings of the 2008 eighth IEEE international conference on data mining. IEEE Computer Society, pp 502–511

  22. Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: EMNLP, vol 14, pp 1532–1543

  23. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 701–710. ACM

  24. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, pp 452–461

  25. Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on world wide web. ACM, pp 811–820

  26. Rendle S, Schmidt-Thieme L (2010) Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the third ACM international conference on web search and data mining. ACM, pp 81–90

  27. Tang D, Qin B, Liu T, Yang Y (2015) User modeling with neural network for review rating prediction. In: IJCAI, pp 1340–1346

  28. Tang J, Qu M, Wang M, Zhang M, Yan, J, Mei, Q (2015) Line: large-scale information network embedding. In: Proceedings of the 24th international conference on world wide web. ACM, pp 1067–1077

  29. 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 15–24

  30. 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 325–334

  31. Yin H, Sun Y, Cui B, Hu Z, Chen L (2013) Lcars: a location-content-aware recommender system. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 221–229

  32. 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 363–372

  33. Zhang JD, Chow CY (2013) igslr: personalized geo-social location recommendation: a kernel density estimation approach. In: Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 334–343

  34. 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 443–452

  35. Zhao S, King I, Lyu MR (2013) Capturing geographical influence in poi recommendations. In: International conference on neural information processing. Springer, pp 530–537

  36. Zhao S, Lyu MR, King I (2016) Aggregated temporal tensor factorization for point-of-interest recommendation. In: International conference on neural information processing. Springer

  37. Zhao S, Zhao T, King I, Lyu MR (2017) Geo-teaser: geo-temporal sequential embedding rank for point-of-interest recommendation. In: Proceedings of the 26th international conference on world wide web companion, pp 153–162

  38. Zhao S, Zhao T, Yang H, Lyu MR, King I (2016) Stellar: spatial-temporal latent ranking for successive point-of-interest recommendation. In: Thirtieth AAAI conference on artificial intelligence

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Acknowledgements

The work described in this paper was partially supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (No. CUHK 14203314 and No. CUHK 14234416 of the General Research Fund), and 2015 Microsoft Research Asia Collaborative Research Program (Project No. FY16-RES-THEME-005).

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

    Shenglin Zhao, Irwin King & Michael R. Lyu

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  1. Shenglin Zhao
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  2. Irwin King
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  3. Michael R. Lyu
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Correspondence to Shenglin Zhao.

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Zhao, S., King, I. & Lyu, M.R. Aggregated Temporal Tensor Factorization Model for Point-of-Interest Recommendation. Neural Process Lett 47, 975–992 (2018). https://doi.org/10.1007/s11063-017-9681-8

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