Advanced Topics in Recommender Systems

  • Charu C. Aggarwal
Chapter

Abstract

Recommender systems are often used in a number of specialized settings that are not covered in previous chapters of this book. In many cases, the recommendations are performed in settings where there might be multiple users or multiple evaluation criteria. For example, consider a scenario where a group of tourists wish to take a vacation together. Therefore, they may want to obtain recommendations that match the overall interests of the various members in the group. In other scenarios, users may use multiple criteria to provide ratings to items. These variations in the problem formulation can sometimes make the prediction problem more challenging. In particular, we will study the following advanced variations of recommender systems in this chapter:

Bibliography

  1. [7]
    G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. Recommender Systems handbook, pp. 217–253, Springer, NY, 2011.Google Scholar
  2. [11]
    G. Adomavicius, N. Manouselis, and Y. Kwon. Multi-criteria recommender systems. Recommender Systems Handbook, Springer, pp. 769–803, 2011.Google Scholar
  3. [12]
    G. Adomavicius and Y. Kwon. New recommendation techniques for multicriteria rating systems. IEEE Intelligent Systems, 22(3), pp. 48–55, 2007.CrossRefGoogle Scholar
  4. [15]
    S. Agarwal. Ranking methods in machine learning. Tutorial at SIAM Conference on Data Mining, 2010. Slides available at: http://www.siam.org/meetings/sdm10/tutorial1.pdf
  5. [18]
    C. Aggarwal. Data classification: algorithms and applications. CRC Press, 2014.Google Scholar
  6. [19]
    C. Aggarwal. Data clustering: algorithms and applications. CRC Press, 2014.Google Scholar
  7. [22]
    C. Aggarwal. Data mining: the textbook. Springer, New York, 2015.Google Scholar
  8. [27]
    C. Aggarwal and P. Yu. On static and dynamic methods for condensation-based privacy-preserving data mining. ACM Transactions on Database Systems (TODS), 33(1), 2, 2008.Google Scholar
  9. [28]
    C. Aggarwal, J. Wolf, and P. Yu. A framework for the optimizing of WWW advertising. Trends in Distributed Systems for Electronic Commerce, pp. 1–10, 1998.Google Scholar
  10. [29]
    C. Aggarwal, S. Gates, and P. Yu. On using partial supervision for text categorization. IEEE Transactions on Knowledge and Data Engineering, 16(2), pp. 245–255, 2004.CrossRefGoogle Scholar
  11. [30]
    C. Aggarwal. On k-anonymity and the curse of dimensionality, Very Large Databases Conference, pp. 901–909, 2005.Google Scholar
  12. [34]
    C. Aggarwal and P. Yu. An automated system for Web portal personalization. Very Large Data Bases Conference, pp. 1031–1040, 2002.Google Scholar
  13. [35]
    D. Agrawal and C. Aggarwal. On the design and quantification of privacy-preserving data mining algorithms. ACM PODS Conference, pp. 247–255, 2001.Google Scholar
  14. [38]
    R. Agrawal, and R. Srikant. Privacy-preserving data mining. ACM SIGMOD Conference, pp. 439–450, 2000.Google Scholar
  15. [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
  16. [57]
    R. Baeza-Yates, C. Hurtado, and M. Mendoza. Query recommendation using query logs in search engines. EDBT 2004 Workshops on Current Trends in Database Technology, pp. 588–596, 2004.Google Scholar
  17. [59]
    S. Balakrishnan and S. Chopra. Collaborative ranking. Web Search and Data Mining Conference, pp. 143–152, 2012.Google Scholar
  18. [75]
    S. Berkovsky, Y. Eytani, T. Kuflik, and F. Ricci. Enhancing privacy and preserving accuracy of a distributed collaborative filtering. ACM Conference on Recommender Systems, pp. 9–16, 2007.Google Scholar
  19. [90]
    P. Boldi, F. Bonchi, C. Castillo, D. Donato, A. Gionis, and S. Vigna. The query-flow graph: model and applications. ACM Conference on Information and Knowledge Management, pp. 609–618, 2008.Google Scholar
  20. [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
  21. [103]
    A. Brun, S. Castagnos, and A. Boyer. Social recommendations: mentor and leader detection to alleviate the cold-start problem in collaborative filtering. Social Network Mining, Analysis, and Research Trends: Techniques and Applications: Techniques and Applications, 270, 2011.Google Scholar
  22. [105]
    A. Broder, M. Fontoura, V. Josifovski, and L. Riedel. A semantic approach to contextual advertising. SIGIR Conference, pp. 559–566, 2007.Google Scholar
  23. [106]
    A. Broder. Computational advertising and recommender systems. ACM Conference on Recommender Systems, pp. 1–2, 2008.Google Scholar
  24. [107]
    A. Broder and V. Josifovski. Introduction to Computational Advertising. Course Material, Stanford University, 2010. http://www.stanford.edu/class/msande239/
  25. [115]
    C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. International Conference on Machine Learning, pp. 89–96, 2005.Google Scholar
  26. [128]
    Z. Cao, T. Qin, T. Liu, M. F. Tsai, and H. Li. Learning to rank: from pairwise approach to listwise approach. International Conference on Machine Learning, pp. 129–137, 2007.Google Scholar
  27. [133]
    J. Canny. Collaborative filtering with privacy via factor analysis. ACM SIGR Conference, pp. 238–245, 2002.Google Scholar
  28. [134]
    I. Cantador and P. Castells. Semantic contextualisation in a news recommender system. Workshop on Context-Aware Recommender Systems, 2009.Google Scholar
  29. [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
  30. [137]
    H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li. Context-aware query suggestion by mining click-through and session data. ACM KDD Conference, pp. 875–883, 2008.Google Scholar
  31. [142]
    D. Chakrabarti, D. Agarwal, and V. Josifovski. Contextual advertising by combining relevance with click feedback. World Wide Web Conference, 2008.Google Scholar
  32. [160]
    W. Chu, L. Li, L. Reyzin, and R. Schapire. Contextual bandits with linear payoff functions. AISTATS Conference, pp. 208–214, 2011.Google Scholar
  33. [168]
    M. O’Connor, D. Cosley, J. Konstan, and J. Riedl. PolyLens: a recommender system for groups of users. European Conference on Computer Supported Cooperative Work, pp. 199–218, 2001.Google Scholar
  34. [175]
    A. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. World Wide Web Conference, pp. 271–280, 2007.Google Scholar
  35. [189]
    C. Dwork. Differential privacy. Encyclopedia of Cryptography and Security, Springer, pp. 338–340, 2011.Google Scholar
  36. [190]
    C. Dwork, R. Kumar, M. Naor, and D. Sivakumar. Rank aggregation methods for the web. World Wide Web Conference, pp. 613–622, 2010.Google Scholar
  37. [192]
    M. Elahi, V. Repsys, and F. Ricci. Rating elicitation strategies for collaborative filtering. E-Commerce and Web Technologies, pp. 160–171, 2011.Google Scholar
  38. [194]
    M. Elahi, M. Braunhofer, F. Ricci, and M. Tkalcic. Personality-based active learning for collaborative filtering recommender systems. Advances in Artificial Intelligence, pp. 360–371, 2013.Google Scholar
  39. [244]
    Q. He, D. Jiang, Z. Liao, S. Hoi, K. Chang, E. Lim, and H. Li. Web query recommendation via sequential query prediction. IEEE International Conference on Data Engineering, pp. 1443–1454, 2009.Google Scholar
  40. [253]
    W. Hong, S. Zheng, H. Wang, and J. Shi. A job recommender system based on user clustering. Journal of Computers, 8(8), 1960–1967, 2013.Google Scholar
  41. [254]
    J. Hopcroft, T. Lou, and J. Tang. Who will follow you back?: reciprocal relationship prediction. ACM International Conference on Information and Knowledge Management, pp. 1137–1146, 2011.Google Scholar
  42. [257]
    N. Houlsby, J. M. Hernandez-Lobato, and Z. Ghahramani. Cold-start active learning with robust ordinal matrix factorization. International Conference on Machine Learning (ICML), pp. 766–774, 2014.Google Scholar
  43. [271]
    A. Jameson and B. Smyth. Recommendation to groups. The Adaptive Web, pp. 596–627, 2007.Google Scholar
  44. [272]
    A. Jameson. More than the sum of its members: challenges for group recommender systems. Proceedings of the working conference on Advanced visual interfaces, pp. 48–54, 2004.Google Scholar
  45. [276]
    D. Jannach, Z. Karakaya, and F. Gedikli. Accuracy improvements for multi-criteria recommender systems. ACM Conference on Electronic Commerce, pp. 674–689, 2012.Google Scholar
  46. [284]
    T. Joachims. Optimizing search engines using click-through data. ACM KDD Conference, pp. 133–142, 2002.Google Scholar
  47. [290]
    S. Kale, L. Reyzin, and R. Schapire. Non-stochastic bandit slate problems. Advances in Neural Information Processing Systems, pp. 1054–1062, 2010.Google Scholar
  48. [295]
    R. Karimi, C. Freudenthaler, A. Nanopoulos, L. Schmidt-Thieme. Exploiting the characteristics of matrix factorization for active learning in recommender systems. ACM Conference on Recommender Systems, pp. 317–320, 2012.Google Scholar
  49. [303]
    J. Kleinberg, C. Papadimitriou, and P. Raghavan. On the value of private information. Proceedings of the 8th Conference on Theoretical Aspects of Rationality and Knowledge, pp. 249–257, 2001.Google Scholar
  50. [314]
    Y. Koren and J. Sill. Collaborative filtering on ordinal user feedback. IJCAI Conference, pp. 3022–3026, 2011.Google Scholar
  51. [323]
    A. Karatzoglou, L. Baltrunas, and Y. Shi. Learning to rank for recommender systems. ACM Conference on Recommender Systems, pp. 493–494, 2013. Slides available at http://www.slideshare.net/kerveros99/learning-to-rank-for-recommender-system-tutorial-acm-recsys-2013
  52. [327]
    A. Lacerda, M. Cristo, W. Fan, N. Ziviani, and B. Ribeiro-Neto. Learning to advertise. ACM SIGIR Conference, pp. 549–556, 2006.Google Scholar
  53. [328]
    K. Lakiotaki, S. Tsafarakis, and N. Matsatsinis. UTA-Rec: a recommender system based on multiple criteria analysis. ACM Conference on Recommender Systems, pp. 219–226, 2008.Google Scholar
  54. [330]
    B. Lamche, U. Trottmann, and W. Worndl. Active learning strategies for exploratory mobile recommender systems. Proceedings of the 4th Workshop on Context-Awareness in Retrieval and Recommendation, pp. 10–17, 2014.Google Scholar
  55. [334]
    N. Lathia, S. Hailes, and L. Capra. Private distributed collaborative filtering using estimated concordance measures. ACM Conference on Recommender Systems, pp. 1–8, 2007.Google Scholar
  56. [340]
    H. Lee and W. Teng. Incorporating multi-criteria ratings in recommendation systems. IEEE International Conference on Information Reuse and Integration (IRI), pp. 273–278, 2007.Google Scholar
  57. [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
  58. [349]
    L. Li, W. Chu, J. Langford, and X. Wang. Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. International Conference on Web Search and Data Mining, pp. 297–306, 2011.Google Scholar
  59. [352]
    N. Li, T. Li, and S. Venkatasubramanian. t-closeness: Privacy beyond k-anonymity and -diversity. IEEE International Conference on Data Enginering, pp. 106–115, 2007.Google Scholar
  60. [353]
    Q. Li, C. Wang, and G. Geng. Improving personalized services in mobile commerce by a novel multicriteria rating approach. World Wide Web Conference, pp. 1235–1236, 2008.Google Scholar
  61. [367]
    N. Liu and Q. Yang. Eigenrank: a ranking-oriented approach to collaborative filtering. ACM SIGIR Conference, pp. 83–90, 2008.Google Scholar
  62. [368]
    N. Liu, M. Zhao, and Q. Yang. Probabilistic latent preference analysis for collaborative filtering. ACM Conference on Information and Knowledge Management, pp. 759–766, 2009.Google Scholar
  63. [370]
    T. Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3), pp. 225–331, 2009.CrossRefGoogle Scholar
  64. [372]
    Z. Liu, Y.-X. Wang, and A. Smola. Fast differentially private matrix factorization. ACM Conference on Recommender Systems, 2015.Google Scholar
  65. [386]
    A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam. -diversity: privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(3), 2007.Google Scholar
  66. [398]
    N. Manouselis and C. Costopoulou. Analysis and classification of multi-criteria recommender systems. World Wide Web, 10(4), pp. 415–441, 2007.CrossRefGoogle Scholar
  67. [399]
    N. Manouselis and Costopoulou. Experimental Analysis of Design Choices in a Multi-Criteria Recommender System. International Journal of Pattern Recognition and AI, 21(2), pp. 311–332, 2007.Google Scholar
  68. [407]
    J. Masthoff. Group recommender systems: combining individual models. Recommender Systems Handbook, Springer, pp. 677–702, 2011.Google Scholar
  69. [408]
    J. Masthoff. Group modeling: Selecting a sequence of television items to suit a group of viewers. Personalized Digital Television, pp. 93–141, 2004.Google Scholar
  70. [409]
    J. Masthoff and A. Gatt. In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Modeling and User-Adapted Interactio, 16(3–4), pp. 281–319, 2006.CrossRefGoogle Scholar
  71. [410]
    J. Masthoff. Modeling the multiple people that are me. International Conference on User Modeling, Also appears in Lecture Notes in Computer Science, Springer, Vol. 2702, pp. 258–262, 2003.MATHGoogle Scholar
  72. [412]
    J. McCarthy and T. Anagnost. MusicFX: An Arbiter of Group Preferences for Computer Supported Collaborative Workouts. ACM Conference on Computer Supported Cooperative Work, pp. 363–372, 1998.Google Scholar
  73. [413]
    K. McCarthy, L. McGinty, B. Smyth, and M. Salamo. The needs of the many: a case-based group recommender system. Advances in Case-Based Reasoning, pp. 196–210, 2004.Google Scholar
  74. [415]
    K. McCarthy, M. Salamo, L. McGinty, B. Smyth, and P. Nicon. Group recommender systems: a critiquing based approach. International Conference on Intelligent User Interfaces, pp. 267–269, 2006.Google Scholar
  75. [429]
    Q. Mei, D. Zhou, and K. Church. Query suggestion using hitting time. ACM Conference on Information and Knowledge Management, pp. 469–478, 2009..Google Scholar
  76. [432]
    A. K. Menon, and C. Elkan. Link prediction via matrix factorization. Machine Learning and Knowledge Discovery in Databases, pp. 437–452, 2011.Google Scholar
  77. [451]
    A. Narayanan and V. Shmatikov. How to break anonymity of the Netflix prize dataset. arXiv preprint cs/0610105, 2006. http://arxiv.org/abs/cs/0610105
  78. [480]
    L. Pizzato, T. Rej, T. Chung, I. Koprinska, and J. Kay. RECON: a reciprocal recommender for online dating. ACM Conference on Recommender systems, pp. 207–214, 2010.Google Scholar
  79. [481]
    L. Pizzato, T. Rej, T. Chung, K. Yacef, I. Koprinska, and J. Kay. Reciprocal recommenders. Workshop on Intelligent Techniques for Web Personalization and Recommender Systems, pp. 20–24, 2010.Google Scholar
  80. [482]
    L. Pizzato, T. Rej, K. Yacef, I. Koprinska, and J. Kay. Finding someone you will like and who won’t reject you. User Modeling, Adaption and Personalization, Springer, pp. 269–280, 2011.Google Scholar
  81. [484]
    H. Polat and W. Du. Privacy-preserving collaborative filtering using randomized perturbation techniques. IEEE International Conference on Data Mining, pp. 625–628, 2003.Google Scholar
  82. [485]
    H. Polat and W. Du. SVD-based collaborative filtering with privacy. ACM symposium on Applied Computing, pp. 791–795, 2005.Google Scholar
  83. [489]
    L. Quijano-Sanchez, J. Recio-Garcia, B. Diaz-Agudo,and G. Jimenez-Diaz. Social factors in group recommender systems. ACM Transactions on Intelligent Systems and Technology (TIST), 4(1), 8, 2013.Google Scholar
  84. [490]
    C. Quoc and V. Le. Learning to rank with nonsmooth cost functions. Advances in Neural Information Processing Systems, 19, pp. 193–200, 2007.Google Scholar
  85. [493]
    S. Rendle. Factorization machines. IEEE International Conference on Data Mining, pp. 995–100, 2010.Google Scholar
  86. [499]
    S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. Uncertainty in Artificial Intelligence (UAI), pp. 452–451, 2009.Google Scholar
  87. [504]
    F. Ricci. Mobile recommender systems. Information Technology and Tourism, 12(3), pp. 205–213, 2010.CrossRefGoogle Scholar
  88. [513]
    N. Rubens, D. Kaplan, and M. Sugiyama. Active learning in recommender systems. Recommender Systems Handbook, Springer, pp. 735–767, 2011.Google Scholar
  89. [514]
    N. Sahoo, R. Krishnan, G. Duncan, and J. Callan. Collaborative filtering with multi-component rating for recommender systems. Proceedings of the sixteenth workshop on information technologies and systems, 2006.Google Scholar
  90. [521]
    P. Samarati. Protecting respondents identities in microdata release. IEEE Transaction on Knowledge and Data Engineering, 13(6), pp. 1010–1027, 2001.CrossRefGoogle Scholar
  91. [545]
    Y. Shi, M. Larson, and A. Hanjalic. List-wise learning to rank with matrix factorization for collaborative filtering. ACM Conference on Recommender Systems, 2010.Google Scholar
  92. [546]
    Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver, and A. Hanjalic. CLiMF: Learning to maximize reciprocal rank with collaborative less-is-more collaborative filtering. ACM Conference on Recommender Systems, pp. 139–146, 2012.Google Scholar
  93. [548]
    Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, and A. Hanjalic. xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance. ACM Conference on Recommender Systems, pp. 431–434, 2013.Google Scholar
  94. [549]
    Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, A. Hanjalic, and N. Oliver. TFMAP: Optimizing MAP for top-n context-aware recommendation. ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 155–164, 2012.Google Scholar
  95. [551]
    R. Shokri, P. Pedarsani, G. Theodorakopoulos, and J. Hubaux. Preserving privacy in collaborative filtering through distributed aggregation of offline profiles. ACM Conference on Recommender Systems, pp. 157–164, 2009.Google Scholar
  96. [578]
    D. Sutherland, B. Poczos, and J. Schneider. Active learning and search on low-rank matrices. ACM KDD Conference, pp. 212–220, 2013.Google Scholar
  97. [579]
    R. Sutton and A. Barto. Reinforcement learning: An introduction, MIT Press, Cambridge, 1998.Google Scholar
  98. [596]
    T. Tang and G. McCalla. The pedagogical value of papers: a collaborative-filtering based paper recommender. Journal of Digital Information, 10(2), 2009.Google Scholar
  99. [604]
    A. Tsoukias, N. Matsatsinis, and K. Lakiotaki. Multi-criteria user modeling in recommender systems. IEEE Intelligent Systems, 26(2), pp. 64–76, 2011.CrossRefGoogle Scholar
  100. [606]
    A. Tveit. Peer-to-peer based recommendations for mobile commerce. Proceedings of the International Workshop on Mobile Commerce, pp. 26–29, 2001.Google Scholar
  101. [621]
    C. Wang, J. Han, Y. Jia, J. Tang, D. Zhang, Y. Yu, and J. Guo. Mining advisor-advisee relationships from research publication networks. ACM KDD Conference, pp. 203–212, 2010.Google Scholar
  102. [624]
    M. Weimer, A. Karatzoglou, Q. Le, and A. Smola. CoFiRank: Maximum margin matrix factorization for collaborative ranking. Advances in Neural Information Processing Systems, 2007.Google Scholar
  103. [625]
    M. Weimer, A. Karatzoglou, and A. Smola. Improving maximum margin matrix factorization. Machine Learning, 72(3), pp. 263–276, 2008.CrossRefGoogle Scholar
  104. [628]
    J. White. Bandit algorithms for Website optimization. O’Reilly Media, Inc, 2012.Google Scholar
  105. [636]
    K. L. Wu, C. C. Aggarwal, and P. S. Yu. Personalization with dynamic profiler. International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems, pp. 12–20, 2001. Also available online as IBM Research Report, RC22004, 2001. Search interface at http://domino.research.ibm.com/library/cyberdig.nsf/index.html
  106. [642]
    Y. Xin and T. Jaakkola. Controlling privacy in recommender systems. Advances in Neural Information Processing Systems, pp. 2618–2626, 2014.Google Scholar
  107. [653]
    Z. Yu, X. Zhou, Y. Hao, and J. Gu. TV program recommendation for multiple viewers based on user profile merging. User Modeling and User-Adapted Interaction, 16(1), pp. 63–82, 2006.CrossRefGoogle Scholar
  108. [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
  109. [657]
    H. Zakerzadeh, C. Aggarwal and K. Barker. Towards breaking the curse of dimensionality for high-dimensional privacy. SIAM Conference on Data Mining, pp. 731–739, 2014.Google Scholar
  110. [714]

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Charu C. Aggarwal
    • 1
  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

Personalised recommendations