Advertisement

Active Learning in Recommender Systems

  • Neil Rubens
  • Mehdi Elahi
  • Masashi Sugiyama
  • Dain Kaplan

Abstract

In Recommender Systems (RS), a user’s preferences are expressed in terms of rated items, where incorporating each rating may improve the RS’s predictive accuracy. In addition to a user rating items at-will (a passive process), RSs may also actively elicit the user to rate items, a process known as Active Learning (AL). However, the number of interactions between the RS and the user is still limited. One aim of AL is therefore the selection of items whose ratings are likely to provide the most information about the user’s preferences. In this chapter, we provide an overview of AL within RSs, discuss general objectives and considerations, and then summarize a variety of methods commonly employed. AL methods are categorized based on our interpretation of their primary motivation/goal, and then sub-classified into two commonly classified types, instance-based and model-based, for easier comprehension. We conclude the chapter by outlining ways in which AL methods could be evaluated, and provide a brief summary of methods performance.

Keywords

Active Learn Recommender System Decision Boundary Training Point Normalize Discount Cumulative Gain 
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.

References

  1. 1.
    Abe, N., Mamitsuka, H.: Query learning strategies using boosting and bagging. In: Proceedings of the Fifteenth International Conference on Machine Learning, vol. 388. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  3. 3.
    Ahn, L.V.: Games with a purpose. Computer 39(6), 92–94 (2006). DOI  10.1109/MC.2006.196 CrossRefGoogle Scholar
  4. 4.
    Bailey, R.A.: Design of Comparative Experiments. Cambridge University Press (2008)Google Scholar
  5. 5.
    Balcan, M.F., Beygelzimer, A., Langford, J.: Agnostic active learning. In: ICML ’06: Proceedings of the 23rd international conference on Machine learning, pp. 65–72. ACM, New York, NY, USA (2006). DOI http://doi.acm.org/10.1145/1143844.1143853
  6. 6.
    Boutilier, C., Zemel, R., Marlin, B.: Active collaborative filtering. In: Proceedings of the Nineteenth Annual Conference on Uncertainty in Artificial Intelligence, pp. 98–106 (2003). URL citeseer.ist.psu.edu/boutilier03active.html
  7. 7.
    Box, G., Hunter, S.J., Hunter, W.G.: Statistics for Experimenters: Design, Innovation, and Discovery. Wiley-Interscience (2005)Google Scholar
  8. 8.
    Breiman, L., Breiman, L.: Bagging predictors. In: Machine Learning, pp. 123–140 (1996)Google Scholar
  9. 9.
    Bridge, D., Ricci, F.: Supporting product selection with query editing recommendations. In: RecSys ’07: Proceedings of the 2007 ACM conference on Recommender systems, pp. 65–72. ACM, New York, NY, USA (2007). DOI http://doi.acm.org/10.1145/1297231.1297243
  10. 10.
    Burke, R.: Evaluating the dynamic properties of recommendation algorithms. In: Proceedings of the fourth ACM conference on Recommender systems, RecSys ’10, pp. 225–228. ACM, New York, NY, USA (2010). DOI http://doi.acm.org/10.1145/1864708.1864753. URL http://doi.acm.org/10.1145/1864708.1864753
  11. 11.
    Carenini, G., Smith, J., Poole, D.: Towards more conversational and collaborative recommender systems. In: IUI ’03: Proceedings of the 8th international conference on Intelligent user interfaces, pp. 12–18. ACM, New York, NY, USA (2003). DOI http://doi.acm.org/10.1145/604045.604052
  12. 12.
    Chan, N.: A-optimality for regression designs. Tech. rep., Stanford University, Department of Statistics (1981)Google Scholar
  13. 13.
    Cohn, D.A.: Neural network exploration using optimal experiment design 6, 679–686 (1994). URL citeseer.ist.psu.edu/article/cohn94neural.html
  14. 14.
    Cohn, D.A., Ghahramani, Z., Jordan, M.I.: Active learning with statistical models. Journal of Artificial Intelligence Research 4, 129–145 (1996)MATHGoogle Scholar
  15. 15.
    Dagan, I., Engelson, S.: Committee-based sampling for training probabilistic classifiers. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 150–157. Citeseer (1995)Google Scholar
  16. 16.
    Danziger, S., Zeng, J., Wang, Y., Brachmann, R., Lathrop, R.: Choosing where to look next in a mutation sequence space: Active learning of informative p53 cancer rescue mutants. Bioinformatics 23(13), 104–114 (2007)CrossRefGoogle Scholar
  17. 17.
    Dasgupta, S., Lee, W., Long, P.: A theoretical analysis of query selection for collaborative filtering. Machine Learning 51, 283–298 (2003). URL citeseer.ist.psu.edu/dasgupta02theoretical.html
  18. 18.
    Diaz-Aviles, E., Drumond, L., Schmidt-Thieme, L., Nejdl, W.: Real-time top-n recommendation in social streams. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys ’12, pp. 59–66. ACM, New York, NY, USA (2012). DOI  10.1145/2365952.2365968. URL http://doi.acm.org/10.1145/2365952.2365968
  19. 19.
    Elahi, M.: Adaptive active learning in recommender systems. In: User Modeling, Adaption and Personalization—19th International Conference, UMAP 2011, Girona, Spain, July 11–15, 2011. Proceedings, pp. 414–417 (2011)Google Scholar
  20. 20.
    Elahi, M., Ricci, F., Rubens, N.: Active learning strategies for rating elicitation in collaborative filtering: a system-wide perspective. ACM Transactions on Intelligent Systems and Technology 5(11) (2013)Google Scholar
  21. 21.
    Ertekin, S., Huang, J., Bottou, L., Giles, L.: Learning on the border: active learning in imbalanced data classification. In: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pp. 127–136. ACM (2007)Google Scholar
  22. 22.
    Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences 55(1), 119–139 (1997)MATHMathSciNetCrossRefGoogle Scholar
  23. 23.
    Fujii, A., Tokunaga, T., Inui, K., Tanaka, H.: Selective sampling for example-based word sense disambiguation. Computational Linguistics 24, 24–4 (1998)Google Scholar
  24. 24.
    Greiner, R., Grove, A., Roth, D.: Learning cost-sensitive active classifiers. Artificial Intelligence 139, 137–174 (2002)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Harpale, A.S., Yang, Y.: Personalized active learning for collaborative filtering. In: SIGIR ’08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 91–98. ACM, New York, NY, USA (2008). DOI http://doi.acm.org/10.1145/1390334.1390352
  26. 26.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’99, pp. 230–237. ACM, New York, NY, USA (1999). DOI http://doi.acm.org/10.1145/312624.312682. URL http://doi.acm.org/10.1145/312624.312682
  27. 27.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004). DOI http://doi.acm.org/10.1145/963770.963772
  28. 28.
    Hinkelmann, K., Kempthorne, O.: Design and Analysis of Experiments, Advanced Experimental Design. Wiley Series in Probability and Statistics (2005)Google Scholar
  29. 29.
    Hofmann, T.: Collaborative filtering via gaussian probabilistic latent semantic analysis. In: SIGIR ’03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pp. 259–266. ACM, New York, NY, USA (2003). DOI http://doi.acm.org/10.1145/860435.860483
  30. 30.
    Huang, Z.: Selectively acquiring ratings for product recommendation. In: ICEC ’07: Proceedings of the ninth international conference on Electronic commerce, pp. 379–388. ACM, New York, NY, USA (2007). DOI http://doi.acm.org/10.1145/1282100.1282171
  31. 31.
    Jin, R., Si, L.: A bayesian approach toward active learning for collaborative filtering. In: AUAI ’04: Proceedings of the 20th conference on Uncertainty in artificial intelligence, pp. 278–285. AUAI Press, Arlington, Virginia, United States (2004)Google Scholar
  32. 32.
    Johar, M., Mookerjee, V., Sarkar, S.: Selling vs. profiling: Optimizing the offer set in web-based personalization. Information Systems Research 25(2), 285–306 (2014).Google Scholar
  33. 33.
    John, R.C.S., Draper, N.R.: D-optimality for regression designs: A review. Technometrics 17(1), 15–23 (1975)MATHMathSciNetCrossRefGoogle Scholar
  34. 34.
    Kale, D., Liu, Y.: Accelerating active learning with transfer learning. In: Data Mining (ICDM), 2013 IEEE 13th International Conference on, pp. 1085–1090 (2013). DOI  10.1109/ICDM.2013.160
  35. 35.
    Kapoor, A., Horvitz, E., Basu, S.: Selective supervision: Guiding supervised learning with decision-theoretic active learning. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pp. 877–882 (2007)Google Scholar
  36. 36.
    Karimi, R., Freudenthaler, C., Nanopoulos, A., Schmidt-Thieme, L.: Exploiting the characteristics of matrix factorization for active learning in recommender systems. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys ’12, pp. 317–320. ACM, New York, NY, USA (2012). DOI  10.1145/2365952.2366031. URL http://doi.acm.org/10.1145/2365952.2366031
  37. 37.
    Kohrs, A., Merialdo, B.: Improving collaborative filtering for new users by smart object selection. In: Proceedings of International Conference on Media Features (ICMF) (2001)Google Scholar
  38. 38.
    Le, Q.T., Tu, M.P.: Active learning for co-clustering based collaborative filtering. In: Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2010 IEEE RIVF International Conference on, pp. 1–4 (2010). DOI  10.1109/RIVF.2010.5633245
  39. 39.
    Leino, J., Räihä, K.J.: Case amazon: ratings and reviews as part of recommendations. In: RecSys ’07: Proceedings of the 2007 ACM conference on Recommender systems, pp. 137–140. ACM, New York, NY, USA (2007). DOI http://doi.acm.org/10.1145/1297231.1297255
  40. 40.
    Lomasky, R., Brodley, C., Aernecke, M., Walt, D., Friedl, M.: Active class selection. In: In Proceedings of the European Conference on Machine Learning (ECML). Springer (2007)Google Scholar
  41. 41.
    McCallum, A., Nigam, K.: Employing em and pool-based active learning for text classification. In: ICML ’98: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 350–358. San Francisco, CA, USA (1998)Google Scholar
  42. 42.
    Mcginty, L., Smyth, B.: On the Role of Diversity in Conversational Recommender Systems. Case-Based Reasoning Research and Development pp. 276–290 (2003)Google Scholar
  43. 43.
    McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI ’06: CHI ’06 extended abstracts on Human factors in computing systems, pp. 1097–1101. ACM Press, New York, NY, USA (2006). DOI http://doi.acm.org/10.1145/1125451.1125659
  44. 44.
    Nakamura, A., Abe, N.: Collaborative filtering using weighted majority prediction algorithms. In: ICML ’98: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 395–403. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1998)Google Scholar
  45. 45.
    Pu, P., Chen, L.: User-Involved Preference Elicitation for Product Search and Recommender Systems. AI magazine pp. 93–103 (2009). URL http://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/2200
  46. 46.
    Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., Riedl, J.: Getting to know you: learning new user preferences in recommender systems. In: IUI ’02: Proceedings of the 7th international conference on Intelligent user interfaces, pp. 127–134. ACM Press, New York, NY, USA (2002). DOI http://doi.acm.org/10.1145/502716.502737
  47. 47.
    Rashid, A.M., Karypis, G., Riedl, J.: Influence in ratings-based recommender systems: An algorithm-independent approach. In: SIAM International Conference on Data Mining, pp. 556–560 (2005)Google Scholar
  48. 48.
    Resnick, P., Sami, R.: The influence limiter: provably manipulation-resistant recommender systems. In: Proceedings of the 2007 ACM conference on Recommender systems, RecSys ’07, pp. 25–32. ACM, New York, NY, USA (2007). DOI http://doi.acm.org/10.1145/1297231.1297236. URL http://doi.acm.org/10.1145/1297231.1297236
  49. 49.
    Ricci, F., Nguyen, Q.N.: Acquiring and revising preferences in a critique-based mobile recommender system. IEEE Intelligent Systems 22(3), 22–29 (2007). DOI http://dx.doi.org/10.1109/MIS.2007.43Google Scholar
  50. 50.
    Rokach, L., Naamani, L., Shmilovici, A.: Pessimistic cost-sensitive active learning of decision trees for profit maximizing targeting campaigns. Data Mining and Knowledge Discovery 17(2), 283–316 (2008). DOI http://dx.doi.org/10.1007/s10618-008-0105-2Google Scholar
  51. 51.
    Roy, N., Mccallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: In Proc. 18th International Conf. on Machine Learning, pp. 441–448. Morgan Kaufmann (2001)Google Scholar
  52. 52.
    Rubens, N., Sugiyama, M.: Influence-based collaborative active learning. In: Proceedings of the 2007 ACM conference on Recommender systems (RecSys 2007). ACM (2007). DOI http://doi.acm.org/10.1145/1297231.1297257
  53. 53.
    Rubens, N., Tomioka, R., Sugiyama, M.: Output divergence criterion for active learning in collaborative settings. IPSJ Transactions on Mathematical Modeling and Its Applications 2(3), 87–96 (2009)Google Scholar
  54. 54.
    Saar-Tsechansky, M., Provost, F.: Decision-centric active learning of binary-outcome models. Information Systems Research 18(1), 4–22 (2007). DOI http://dx.doi.org/10.1287/isre.1070.0111Google Scholar
  55. 55.
    Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: SIGIR ’02: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 253–260. ACM, New York, NY, USA (2002). DOI http://doi.acm.org/10.1145/564376.564421
  56. 56.
    Schohn, G., Cohn, D.: Less is more: Active learning with support vector machines. In: Proc. 17th International Conf. on Machine Learning, pp. 839–846. Morgan Kaufmann, San Francisco, CA (2000). URL citeseer.ist.psu.edu/schohn00less.html
  57. 57.
    Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison (2009)Google Scholar
  58. 58.
    Settles, B., Craven, M.: An analysis of active learning strategies for sequence labeling tasks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1069–1078. ACL Press (2008)Google Scholar
  59. 59.
    Settles, B., Craven, M., Friedland, L.: Active learning with real annotation costs. In: Proceedings of the NIPS Workshop on Cost-Sensitive Learning, pp. 1–10 (2008)Google Scholar
  60. 60.
    Settles, B., Craven, M., Ray, S.: Multiple-instance active learning. In: Advances in Neural Information Processing Systems (NIPS), vol. 20, pp. 1289–1296. MIT Press (2008)Google Scholar
  61. 61.
    Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. In: Computational Learning Theory, pp. 287–294 (1992). URL citeseer.ist.psu.edu/seung92query.html
  62. 62.
    Sugiyama, M.: Active learning in approximately linear regression based on conditional expectation of generalization error. Journal of Machine Learning Research 7, 141–166 (2006)MATHMathSciNetGoogle Scholar
  63. 63.
    Sugiyama, M., Rubens, N.: A batch ensemble approach to active learning with model selection. Neural Netw. 21(9), 1278–1286 (2008). DOI http://dx.doi.org/10.1016/j.neunet.2008.06.004Google Scholar
  64. 64.
    Sugiyama, M., Rubens, N., Müller, K.R.: Dataset Shift in Machine Learning, chap. A conditional expectation approach to model selection and active learning under covariate shift. MIT Press, Cambridge (2008)Google Scholar
  65. 65.
    Sutherland, D.J., Póczos, B., Schneider, J.: Active learning and search on low-rank matrices. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’13, pp. 212–220. ACM, New York, NY, USA (2013). DOI  10.1145/2487575.2487627. URL http://doi.acm.org/10.1145/2487575.2487627
  66. 66.
    Swearingen, K., Sinha, R.: Beyond algorithms: An hci perspective on recommender systems. ACM SIGIR 2001 Workshop on Recommender Systems (2001). URL http://citeseer.ist.psu.edu/cache/papers/cs/31330/http:zSzzSzweb.engr.oregonstate.eduzSz~herlockzSzrsw2001zSzfinalzSzfull_length_paperszSz4_swearingenzPz.pdf/swearingen01beyond.pdf
  67. 67.
    Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. In: P. Langley (ed.) Proceedings of ICML-00, 17th International Conference on Machine Learning, pp. 999–1006. Morgan Kaufmann Publishers, San Francisco, US, Stanford, US (2000). URL citeseer.ist.psu.edu/article/tong01support.html
  68. 68.
    Yu, K., Bi, J., Tresp, V.: Active learning via transductive experimental design. In: Proceedings of the 23rd Int. Conference on Machine Learning ICML ’06, pp. 1081–1088. ACM, New York, NY, USA (2006). DOI http://doi.acm.org/10.1145/1143844.1143980
  69. 69.
    Zhao, L., Pan, S.J., Xiang, E.W., Zhong, E., Lu, Z., Yang, Q.: Active transfer learning for cross-system recommendation. In: AAAI (2013)Google Scholar
  70. 70.
    Zhao, X., Zhang, W., Wang, J.: Interactive collaborative filtering. In: Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, CIKM ’13, pp. 1411–1420. ACM, New York, NY, USA (2013). DOI  10.1145/2505515.2505690. URL http://doi.acm.org/10.1145/2505515.2505690

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Neil Rubens
    • 1
  • Mehdi Elahi
    • 2
  • Masashi Sugiyama
    • 3
  • Dain Kaplan
    • 3
  1. 1.University of Electro-CommunicationsTokyoJapan
  2. 2.Free University of Bozen-BolzanoBolzanoItaly
  3. 3.Tokyo Institute of TechnologyTokyoJapan

Personalised recommendations