Time- and Location-Sensitive Recommender Systems



In many real scenarios, the buying and rating behaviors of customers are associated with temporal information. For example, the ratings in the Netflix Prize data set are associated with a “GradeDate” variable, and it was eventually shown [310] how the temporal component could be used to improve the rating predictions.


Markov Model Recommender System Temporal Context Sequential Pattern Mining Recommendation Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. [6]
    G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems, 23(1), pp. 103–145, 2005.CrossRefGoogle Scholar
  2. [7]
    G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. Recommender Systems handbook, pp. 217–253, Springer, NY, 2011.Google Scholar
  3. [22]
    C. Aggarwal. Data mining: the textbook. Springer, New York, 2015.Google Scholar
  4. [23]
    C. Aggarwal and J. Han. Frequent pattern mining. Springer, New York, 2014.Google Scholar
  5. [37]
    R. Agrawal and R. Srikant. Mining sequential patterns. International Conference on Data Engineering, pp. 3–14, 1995.Google Scholar
  6. [40]
    H. Ahn, K. Kim, and I. Han. Mobile advertisement recommender system using collaborative filtering: MAR-CF. Proceedings of the 2006 Conference of the Korea Society of Management Information Systems, 2006.Google Scholar
  7. [52]
    L. Ardissono, A. Goy, G. Petrone, M. Segnan, and P. Torasso. INTRIGUE: personalized recommendation of tourist attractions for desktop and hand-held devices. Applied Artificial Intelligence, 17(8), pp. 687–714, 2003.CrossRefGoogle Scholar
  8. [53]
    W. G. Aref and H. Samet. Efficient processing of window queries in the pyramid data structure. ACM PODS Conference, pp. 265–272, 1990.Google Scholar
  9. [54]
    D. Ashbrook and T. Starner. Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 7(5), pp. 275–286, 2003.CrossRefGoogle Scholar
  10. [61]
    L. Baltrunas and X. Amatriain. Towards time-dependant recommendation based on implicit feedback. RecSys Workshop on Context-Aware Recommender Systems, 2009.Google Scholar
  11. [64]
    J. Bao, Y. Zheng, and M. Mokbel. Location-based and preference-aware recommendation using sparse geo-social networking data. International Conference on Advances in Geographic Information Systems, pp. 199–208, 2012.Google Scholar
  12. [67]
    A. Bar, L. Rokach, G. Shani, B. Shapira, and A. Schclar. Boosting simple collaborative filtering models using ensemble methods. Arxiv Preprint, arXiv:1211.2891, 2012. Also appears in Multiple Classifier Systems, Springer, pp. 1–12, 2013.
  13. [89]
    F. Bohnert, I. Zukerman, S. Berkovsky, T. Baldwin, and L. Sonenberg. Using interest and transition models to predict visitor locations in museums. AI Communications, 2(2), pp. 195–202, 2008.MathSciNetzbMATHGoogle Scholar
  14. [92]
    B. Bouneffouf, A. Bouzeghoub, and A. Gancarski. A contextual-bandit algorithm for mobile context-aware recommender system. Neural Information Processing, pp. 324–331, 2012.Google Scholar
  15. [100]
    A. Brenner, B. Pradel, N. Usunier, and P. Gallinari. Predicting most rated items in weekly recommendation with temporal regression. Workshop on Context-Aware Movie Recommendation, pp. 24–27, 2010.Google Scholar
  16. [108]
    M. Brunato and R. Battiti. PILGRIM: A location broker and mobility-aware recommendation system. International Conference on Pervasive Computing and Communications, pp. 265–272, 2003.Google Scholar
  17. [109]
    P. Brusilovsky, A. Kobsa, and W. Nejdl. The adaptive web: methods and strategies of web personalization, Lecture Notes in Computer Sceince, Vol. 4321, Springer, 2007.Google Scholar
  18. [130]
    P. Campos, F. Diez, and I. Cantador. Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Modeling and User-Adapted Interaction, 24(1–2), pp. 67–119, 2014.CrossRefGoogle Scholar
  19. [131]
    P. Campos, A. Bellogin, F. Diez, and J. Chavarriaga. Simple time-biased KNN-based recommendations. Workshop on Context-Aware Movie Recommendation, pp. 20–23, 2010.Google Scholar
  20. [136]
    H. Cao, E. Chen, J. Yang, and H. Xiong. Enhancing recommender systems under volatile user interest drifts. ACM Conference on Information and Knowledge Management, pp. 1257–1266, 2009.Google Scholar
  21. [156]
    K. Cheverst, N. Davies, K. Mitchell, A. Friday, and C. Efstratiou. Developing a context-aware electronic tourist guide: some issues and experiences. ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 17–24, 2000.Google Scholar
  22. [169]
    R. Cooley, B. Mobasher, and J. Srivastava. Data preparation for mining World Wide Web browsing patterns. Knowledge and Information Systems, 1(1), pp. 5–32, 1999.CrossRefGoogle Scholar
  23. [177]
    B. De Carolis, I. Mazzotta, N. Novielli, and V. Silvestri. Using common sense in providing personalized recommendations in the tourism domain. Workshop on Context-Aware Recommender Systems, 2009.Google Scholar
  24. [182]
    M. Deshpande and G. Karypis. Selective Markov models for predicting Web page accesses. ACM Transactions on Internet Technology (TOIT), 4(2), pp. 163–184, 2004.CrossRefGoogle Scholar
  25. [185]
    Y. Ding and X. Li. Time weight collaborative filtering. ACM International Conference on Information and Knowledge Management, pp. 485–492, 2005.Google Scholar
  26. [186]
    Y. Ding, X. Li, and M. Orlowska. Recency-based collaborative filtering. Australasian Database Conference, pp. 99–107, 2009.Google Scholar
  27. [202]
    R. A. Finkel and J. L. Bentley. Quad trees: A data structure for retrieval on composite keys. Acta Informatica, 4, pp. 1–9, 1974.CrossRefzbMATHGoogle Scholar
  28. [208]
    X. Fu, J. Budzik, and K. J. Hammond. Mining navigation history for recommendation. International Conference on Intelligent User Interfaces, 2000.Google Scholar
  29. [212]
    Z. Gantner, S. Rendle, and L. Schmidt-Thieme. Factorization models for context-/time-aware movie recommendations. Workshop on Context-Aware Movie Recommendation, pp. 14–19, 2010.Google Scholar
  30. [213]
    A. Garcia-Crespo, J. Chamizo, I. Rivera, M. Mencke, R. Colomo-Palacios, and J. M. Gomez-Berbis. SPETA: Social pervasive e-Tourism advisor. Telematics and Informatics 26(3), pp. 306–315. 2009.CrossRefGoogle Scholar
  31. [218]
    M. Gery and H. Haddad. Evaluation of Web usage mining approaches for user’s next request prediction. ACM international workshop on Web information and data management, pp. 74–81, 2003.Google Scholar
  32. [230]
    S. Gordea and M. Zanker. Time filtering for better recommendations with small and sparse rating matrices. International Conference on Web Information Systems Engineering, pp. 171–183, 2007.Google Scholar
  33. [231]
    M. Gorgoglione and U. Panniello. Including context in a transactional recommender system using a pre- filtering approach: two real e-commerce applications. International Conference on Advanced Information Networking and Applications Workshops, pp. 667–672, 2009.Google Scholar
  34. [249]
    C. Hermann. Time-based recommendations for lecture materials. World Conference on Educational Multimedia, Hypermedia and Telecommunications, pp. 1028–1033, 2010.Google Scholar
  35. [265]
    D. Isaacson and R. Madsen. Markov chains, theory and applications, Wiley, 1976.Google Scholar
  36. [266]
    M. Jahrer, A. Toscher, and R. Legenstein. Combining predictions for accurate recommender systems. ACM KDD Conference, pp. 693–702, 2010.Google Scholar
  37. [293]
    A. Karatzoglou. Collaborative temporal order modeling. ACM Conference on Recommender Systems, pp. 313–316, 2011.Google Scholar
  38. [294]
    A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering. ACM Conference on Recommender Systems, pp. 79–86, 2010.Google Scholar
  39. [296]
    J. Kemeny and J. Snell. Finite Markov chains. Springer, New York, 1983.Google Scholar
  40. [304]
    N. Koenigstein, G. Dror, and Y. Koren. Yahoo! Music recommendations: modeling music ratings with temporal dynamics and item taxonomy. ACM Conference on Recommender Systems, pp. 165–172, 2011.Google Scholar
  41. [310]
    Y. Koren. Collaborative filtering with temporal dynamics. ACM KDD Conference, pp. 447–455, 2009. Another version also appears in the Communications of the ACM,, 53(4), pp. 89–97, 2010.Google Scholar
  42. [312]
    Y. Koren and R. Bell. Advances in collaborative filtering. Recommender Systems Handbook, Springer, pp. 145–186, 2011. (Extended version in 2015 edition of handbook).Google Scholar
  43. [318]
    J. Krosche, J. Baldzer, and S. Boll. MobiDENK -mobile multimedia in monument conservation. IEEE MultiMedia, 11(2), pp. 72–77, 2004.CrossRefGoogle Scholar
  44. [319]
    A. Krogh, M. Brown, I. Mian, K. Sjolander, and D. Haussler. Hidden Markov models in computational biology: Applications to protein modeling. Journal of molecular biology, 235(5), pp. 1501–1531, 1994.CrossRefGoogle Scholar
  45. [332]
    L. Lathauwer, B. Moor, and J. Vandewalle. A multilinear singular value decomposition. SIAM Journal on Matrix Analysis and Applications, 21(4), pp. 1253–1278. 2000.MathSciNetCrossRefzbMATHGoogle Scholar
  46. [333]
    N. Lathia, S. Hailes, and L. Capra. Temporal collaborative filtering with adaptive neighbourhoods. ACM SIGIR Conference, pp. 796–797, 2009.Google Scholar
  47. [335]
    N. Lathia, S. Hailes, L. Capra, and X. Amatriain. Temporal diversity in recommender systems. ACM SIGIR Conference, pp. 210–217, 2010.Google Scholar
  48. [337]
    D. Lee, S. Park, M. Kahng, S. Lee, and S. Lee. Exploiting contextual information from event logs for personalized recommendation. Chapter in Computer and Information Science, Springer, 2010.Google Scholar
  49. [343]
    J. Levandoski, M. Sarwat, A. Eldawy, and M. Mokbel. LARS: A location-aware recommender system. IEEE ICDE Conference, pp. 450–461, 2012.Google Scholar
  50. [348]
    L. Li, W. Chu, J. Langford, and R. Schapire. A contextual-bandit approach to personalized news article recommendation. World Wide Web Conference, pp. 661–670, 2010.Google Scholar
  51. [366]
    N. Liu, M. Zhao, E. Xiang, and Q Yang. Online evolutionary collaborative filtering. ACM Conference on Recommender Systems, pp. 95–102, 2010.Google Scholar
  52. [435]
    S. Min and I. Han. Detection of the customer time-variant pattern for improving recommender systems. Expert Systems and Applications, 28(2), pp. 189–199, 2005.CrossRefGoogle Scholar
  53. [440]
    B. Mobasher, R. Cooley, and J. Srivastava. Automatic personalization based on Web usage mining. Communications of the ACM, 43(8), pp. 142–151, 2000.CrossRefGoogle Scholar
  54. [442]
    B. Mobasher, H. Dai, T. Luo, and H. Nakagawa. Using sequential and non-sequential patterns in predictive web usage mining tasks. International Conference on Data Mining, pp. 669–672, 2002.Google Scholar
  55. [443]
    B. Mobasher, H. Dai, M. Nakagawa, and T. Luo. Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery, 6: pp. 61–82, 2002.MathSciNetCrossRefGoogle Scholar
  56. [447]
    M. Mokbel and J. Levandoski. Toward context and preference-aware location-based services. ACM International Workshop on Data Engineering for Wireless and Mobile Access, pp. 25–32, 2009.Google Scholar
  57. [458]
    K. Oku, S. Nakajima, J. Miyazaki, and S. Uemura. Context-aware SVM for context-dependent information recommendation. International Conference on Mobile Data Management, pp. 109–109, 2006.Google Scholar
  58. [464]
    M. Park, J. Hong, and S. Cho. Location-based recommendation system using Bayesian user’s preference model in mobile devices. Ubiquitous Intelligence and Computing, pp. 1130–1139, 2007.Google Scholar
  59. [471]
    U. Panniello, A. Tuzhilin, M. Gorgoglione, C. Palmisano, and A. Pedone. Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. ACM Conference on Recommender Systems, pp. 265–268, 2009.Google Scholar
  60. [479]
    J. Pitkow and P. Pirolli. Mining longest repeating subsequences to predict WWW surfing. USENIX Annual Technical Conference, 1999.Google Scholar
  61. [495]
    S. Rendle. Context-aware ranking with factorization models. Studies in Computational Intelligence, Chapter 9, Springer, 2011.Google Scholar
  62. [496]
    S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme. Fast context-aware recommendations with factorization machines. ACM SIGIR Conference, pp. 635–644, 2011.Google Scholar
  63. [504]
    F. Ricci. Mobile recommender systems. Information Technology and Tourism, 12(3), pp. 205–213, 2010.CrossRefGoogle Scholar
  64. [515]
    A. Said, S. Berkovsky, and E. de Luca. Putting things in context: challenge on context-aware movie recommendation. Proceedings of the Workshop on Context-Aware Movie Recommendation, 2010.Google Scholar
  65. [532]
    S. Schechter, M. Krishnan, and M. D. Smith. Using path profiles to predict http requests. World Wide Web Conference, 1998.Google Scholar
  66. [562]
    J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan. Web usage mining: discovery and applications of usage patterns from Web data. ACM SIGKDD Explorations, 1(2), pp. 12–23, 2000.CrossRefGoogle Scholar
  67. [567]
    H. Stormer. Improving e-commerce recommender systems by the identification of seasonal products. Conference on Artificial Intelligence, pp. 92–99, 2007.Google Scholar
  68. [595]
    T. Tang, P. Winoto, and K. C. C. Chan. On the temporal analysis for improved hybrid recommendations. International Conference on Web Intelligence, pp. 214–220, 2003.Google Scholar
  69. [611]
    M. van Setten, S. Pokraev, and J. Koolwaaij. Context-aware recommendations in the mobile tourist application compass. Adaptive Hypermedia, Springer, pp. 235–244, 2004.Google Scholar
  70. [618]
    V. Vlahakis, N. Ioannidis, J. Karigiannis, M. Tsotros, M. Gounaris, D. Stricker, T. Gleue, P. Daehne, and L. Almeida. Archeoguide: an augmented reality guide for archaeological sites. IEEE Computer Graphics and Applications, 22(5), pp. 52–60, 2002.CrossRefGoogle Scholar
  71. [626]
    S.-S. Weng, L. Binshan, and W.-T. Chen. Using contextual information and multidimensional approach for recommendation. Expert Systems and Applications, 36, pp. 1268–1279, 2009.CrossRefGoogle Scholar
  72. [633]
    W. Woerndl, C. Schueller, and R. Wojtech. A hybrid recommender system for context-aware recommendations of mobile applications. IEEE International Conference on Data Engineering Workshop, pp. 871–878, 2007.Google Scholar
  73. [635]
    P. Wu, C. Yeung, W. Liu, C. Jin, and Y. Zhang. Time-aware collaborative filtering with the piecewise decay function. arXiv preprint, arXiv:1010.3988, 2010.
  74. [639]
    L. Xiang, Q. Yuan, S. Zhao, L. Chen, X. Zhang, Q. Yang, and J. Sun. Temporal recommendation on graphs via long-and short-term preference fusion. ACM KDD Conference, pp. 723–732, 2010.Google Scholar
  75. [645]
    W. Yang, H. Cheng, and J. Dia. A location-aware recommender system for mobile shopping environments. Expert Systems with Applications, 34(1), pp. 437–445, 2008.CrossRefGoogle Scholar
  76. [649]
    H. Yin, Y. Sun, B. Cui, Z. Hu, and L. Chen. LCARS: A location-content-aware recommender system. ACM KDD Conference, pp. 221–229, 2013.Google Scholar
  77. [654]
    Z. Yu, X. Zhou, D. Zhang, C. Y. Chin, and X. Wang. Supporting context-aware media recommendations for smart phones. IEEE Pervasive Computing, 5(3), pp. 68–75, 2006.CrossRefGoogle Scholar
  78. [655]
    Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. Thalmann. Time-aware point-of-interest recommendation. ACM SIGIR Conference, pp. 363–372, 2013.Google Scholar
  79. [684]
    A. Zimdars, D. Chickering, and C. Meek. Using temporal data for making recommendations. Uncertainty in Artificial Intelligence, pp. 580–588, 2001.Google Scholar
  80. [685]
    A. Zimmermann, M. Specht, and A. Lorenz. Personalization and context management. User Modeling and User-Adapted Interaction, 15(3–4), pp. 275–302, 2005.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

Personalised recommendations