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Rating-Based Collaborative Filtering: Algorithms and Evaluation

  • Daniel Kluver
  • Michael D. Ekstrand
  • Joseph A. Konstan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10100)

Abstract

Recommender systems help users find information by recommending content that a user might not know about, but will hopefully like. Rating-based collaborative filtering recommender systems do this by finding patterns that are consistent across the ratings of other users. These patterns can be used on their own, or in conjunction with other forms of social information access to identify and recommend content that a user might like. This chapter reviews the concepts, algorithms, and means of evaluation that are at the core of collaborative filtering research and practice. While there are many recommendation algorithms, the ones we cover serve as the basis for much of past and present algorithm development. After presenting these algorithms we present examples of two more recent directions in recommendation algorithms: learning-to-rank and ensemble recommendation algorithms. We finish by describing how collaborative filtering algorithms can be evaluated, and listing available resources and datasets to support further experimentation. The goal of this chapter is to provide the basis of knowledge needed for readers to explore more advanced topics in recommendation.

References

  1. 1.
    Aggarwal, C.C., Wolf, J.L., Wu, K.L., Yu, P.S.: Horting hatches an egg: a new graph-theoretic approach to collaborative filtering. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 1999, pp. 201–212. ACM. (1999).  https://doi.org/10.1145/312129.312230
  2. 2.
    Amatriain, X., Basilico, J.: Netflix recommendations: beyond the 5 stars (part 1). http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html
  3. 3.
    Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: Proceedings of the 2007 Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 43–52. IEEE Computer Society (2007).  https://doi.org/10.1109/ICDM.2007.90
  4. 4.
    Bellogin, A.: Performance prediction and evaluation in recommender systems: an information retrieval perspective. Ph.D. thesis. Universidad Autnoma de Madrid (2012)Google Scholar
  5. 5.
    Bellogin, A., Castells, P., Cantador, I.: Precision-oriented evaluation of recommender systems: an algorithmic comparison. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 333–336. ACM (2011).  https://doi.org/10.1145/2043932.2043996
  6. 6.
    Bieganski, P., Konstan, J., Riedl, J.: System, method and article of manufacture for making serendipity-weighted recommendations to a user, 25 December 2001. US Patent 6,334,127Google Scholar
  7. 7.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022. http://dl.acm.org/citation.cfm?id=944919.944937
  8. 8.
    Bogers, T.: Tag-based recommendation. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 441–479. Springer, Cham (2018)Google Scholar
  9. 9.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. Technical report MSR-TR-98-12, Microsoft Research, May 1998. http://research.microsoft.com/apps/pubs/default.aspx?id=69656
  10. 10.
    Breiman, L.: Stacked regressions. Mach. Learn. 24(1), 49–64 (1996).  https://doi.org/10.1023/A:1018046112532CrossRefzbMATHGoogle Scholar
  11. 11.
    Celma, Ò., Herrera, P.: A new approach to evaluating novel recommendations. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, pp. 179–186. ACM (2008).  https://doi.org/10.1145/1454008.1454038
  12. 12.
    Chen, T., Zhang, W., Lu, Q., Chen, K., Zheng, Z., Yu, Y.: SVDFeature: a toolkit for feature-based collaborative filtering. J. Mach. Learn. Res. 13(1), 3619–3622 (2012). http://dl.acm.org/citation.cfm?id=2503308.2503357MathSciNetzbMATHGoogle Scholar
  13. 13.
    Cremonesi, P., Garzottto, F., Turrin, R.: User effort vs. accuracy in rating-based elicitation. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys 2012, pp. 27–34. ACM (2012).  https://doi.org/10.1145/2365952.2365963
  14. 14.
    Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems RecSys 2010, pp. 39–46. ACM (2010).  https://doi.org/10.1145/1864708.1864721
  15. 15.
    Ekstrand, M.: Similarity functions for user-user collaborative filtering. http://grouplens.org/blog/similarity-functions-for-user-user-collaborative-filtering/
  16. 16.
    Ekstrand, M.: Similarity functions in item-item CF. https://md.ekstrandom.net/blog/2015/06/item-similarity/
  17. 17.
    Ekstrand, M., Riedl, J.: When recommenders fail: predicting recommender failure for algorithm selection and combination. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys 2012, pp. 233–236. ACM (2012).  https://doi.org/10.1145/2365952.2366002
  18. 18.
    Ekstrand, M.D., Harper, F.M., Willemsen, M.C., Konstan, J.A.: User perception of differences in recommender algorithms. In: Proceedings of the 8th ACM Conference on Recommender Systems, RecSys 2014, pp. 161–168. ACM (2014).  https://doi.org/10.1145/2645710.2645737
  19. 19.
    Ekstrand, M.D., Kluver, D., Harper, F.M., Konstan, J.A.: Letting users choose recommender algorithms: an experimental study. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys 2015, pp. 11–18. ACM (2015).  https://doi.org/10.1145/2792838.2800195
  20. 20.
    Ekstrand, M.D., Ludwig, M., Konstan, J.A., Riedl, J.T.: Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 133–140. ACM (2011).  https://doi.org/10.1145/2043932.2043958
  21. 21.
    Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum.-Comput. Interact. 4(2), 81–173 (2011).  https://doi.org/10.1561/1100000009CrossRefGoogle Scholar
  22. 22.
    Funk, S.: Netflix update: try this at home. http://sifter.org/~simon/journal/20061211.html
  23. 23.
    Golbandi, N., Koren, Y., Lempel, R.: Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 595–604. ACM (2011).  https://doi.org/10.1145/1935826.1935910
  24. 24.
    Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inf. Retrieval 4(2), 133–151 (2001).  https://doi.org/10.1023/A:1011419012209CrossRefzbMATHGoogle Scholar
  25. 25.
    Guy, I.: People recommendation on social media. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 570–623. Springer, Cham (2018)Google Scholar
  26. 26.
    Harper, F.M., Konstan, J.A.: The MovieLens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19:1–19:19 (2015).  https://doi.org/10.1145/2827872CrossRefGoogle Scholar
  27. 27.
    Herlocker, J., Konstan, J.A., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retrieval 5(4), 287–310 (2002).  https://doi.org/10.1023/A:1020443909834CrossRefGoogle Scholar
  28. 28.
    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).  https://doi.org/10.1145/963770.963772CrossRefGoogle Scholar
  29. 29.
    Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1995, pp. 194–201. ACM Press/Addison-Wesley Publishing Co. (1995).  https://doi.org/10.1145/223904.223929
  30. 30.
    Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1999, pp. 50–57. ACM (1999).  https://doi.org/10.1145/312624.312649
  31. 31.
    Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22(1), 89–115 (2004).  https://doi.org/10.1145/963770.963774CrossRefGoogle Scholar
  32. 32.
    Huang, Z., Chung, W., Chen, H.: A graph model for E-commerce recommender systems. J. Am. Soc. Inf. Sci. Technol. 55(3), 259–274 (2004).  https://doi.org/10.1002/asi.10372CrossRefGoogle Scholar
  33. 33.
    Jamali, M., Ester, M.: TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 397–406. ACM (2009).  https://doi.org/10.1145/1557019.1557067
  34. 34.
    Jannach, D., Lerche, L., Gedikli, F., Bonnin, G.: What recommenders recommend – an analysis of accuracy, popularity, and sales diversity effects. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 25–37. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-38844-6_3CrossRefGoogle Scholar
  35. 35.
    Jannach, D., Lerche, L., Zanker, M.: Recommending based on implicit feedback. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 510–569. Springer, Cham (2018)Google Scholar
  36. 36.
    Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002).  https://doi.org/10.1145/582415.582418CrossRefGoogle Scholar
  37. 37.
    Kluver, D., Konstan, J.A.: Evaluating recommender behavior for new users. In: Proceedings of the 8th ACM Conference on Recommender Systems, RecSys 2014, pp. 121–128. ACM (2014).  https://doi.org/10.1145/2645710.2645742
  38. 38.
    Kluver, D., Nguyen, T.T., Ekstrand, M., Sen, S., Riedl, J.: How many bits per rating? In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys 2012, pp. 99–106. ACM (2012).  https://doi.org/10.1145/2365952.2365974
  39. 39.
    Knijnenburg, B., Willemsen, M., Gantner, Z., Soncu, H., Newell, C.: Explaining the user experience of recommender systems. User Model. User-Adap. Interact. 22(4), 441–504.  https://doi.org/10.1007/s11257-011-9118-4
  40. 40.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 426–434. ACM.  https://doi.org/10.1145/1401890.1401944
  41. 41.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009).  https://doi.org/10.1109/MC.2009.263CrossRefGoogle Scholar
  42. 42.
    Lathia, N., Hailes, S., Capra, L.: Temporal collaborative filtering with adaptive neighbourhoods. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, pp. 796–797. ACM (2009).  https://doi.org/10.1145/1571941.1572133
  43. 43.
    Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal diversity in recommender systems. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 210–217. ACM (2010).  https://doi.org/10.1145/1835449.1835486
  44. 44.
    Lee, D., Brusilovsky, P.: Recommendations based on social links. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 391–440. Springer, Cham (2018)Google Scholar
  45. 45.
    Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. In: Proceedings of SIAM Data Mining (SDM 2005) (2005). https://arxiv.org/abs/cs/0702144CrossRefGoogle Scholar
  46. 46.
    Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems, RecSys 2007, pp. 17–24. ACM (2007).  https://doi.org/10.1145/1297231.1297235
  47. 47.
    McAuley, J., Leskovec, J.: Hidden factors and hidden topics: Understanding rating dimensions with review text. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 165–172. RecSys 2013. ACM (2013).  https://doi.org/10.1145/2507157.2507163
  48. 48.
    McNee, S.M., Lam, S.K., Konstan, J.A., Riedl, J.: Interfaces for eliciting new user preferences in recommender systems. In: Brusilovsky, P., Corbett, A., de Rosis, F. (eds.) UM 2003. LNCS (LNAI), vol. 2702, pp. 178–187. Springer, Heidelberg (2003).  https://doi.org/10.1007/3-540-44963-9_24CrossRefzbMATHGoogle Scholar
  49. 49.
    Ning, X., Karypis, G.: Slim: Sparse linear methods for top-n recommender systems. In: Proceedings of the IEEE 11th International Conference on Data Mining, ICDM 2011, pp. 497–506, December 2011.  https://doi.org/10.1109/ICDM.2011.134
  50. 50.
    Olson, J.S., Kellogg, W.A. (eds.): Ways of Knowing in HCI. Springer, New York (2014).  https://doi.org/10.1007/978-1-4939-0378-8CrossRefGoogle Scholar
  51. 51.
    O’Mahoney, M., Smyth, B.: From opinions to recommendations. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 480–509. Springer, Cham (2018)Google Scholar
  52. 52.
    Phuong, N.D., Thang, L.Q., Phuong, T.M.: A graph-based method for combining collaborative and content-based filtering. In: Ho, T.-B., Zhou, Z.-H. (eds.) PRICAI 2008. LNCS (LNAI), vol. 5351, pp. 859–869. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-89197-0_80CrossRefGoogle Scholar
  53. 53.
    Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 157–164. ACM (2011).  https://doi.org/10.1145/2043932.2043962
  54. 54.
    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: Proceedings of the 7th International Conference on Intelligent User Interfaces, IUI 2002, pp. 127–134. ACM (2002).  https://doi.org/10.1145/502716.502737
  55. 55.
    Rashid, A.M., Karypis, G., Riedl, J.: Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explor. Newslett. 10(2), 90–100 (2008).  https://doi.org/10.1145/1540276.1540302CrossRefGoogle Scholar
  56. 56.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2009, pp. 452–461. AUAI Press. http://dl.acm.org/citation.cfm?id=1795114.1795167
  57. 57.
    Rendle, S., Schmidt-Thieme, L.: Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, pp. 251–258. ACM (2008).  https://doi.org/10.1145/1454008.1454047
  58. 58.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW 1994, pp. 175–186. ACM (1994).  https://doi.org/10.1145/192844.192905
  59. 59.
    Rogers, S.K.: Item-to-item recommendations at Pinterest. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys 2016, pp. 393–393. ACM (2016).  https://doi.org/10.1145/2959100.2959130
  60. 60.
    Said, A., Fields, B., Jain, B.J., Albayrak, S.: User-centric evaluation of a k-furthest neighbor collaborative filtering recommender algorithm. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, CSCW 2013, pp. 1399–1408. ACM (2013).  https://doi.org/10.1145/2441776.2441933
  61. 61.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW 2001, pp. 285–295. ACM.  https://doi.org/10.1145/371920.372071
  62. 62.
    Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.T.: Application of dimensionality reduction in recommender system - a case study. In: WebKDD 2000. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.8381
  63. 63.
    Shan, H., Banerjee, A.: Generalized probabilistic matrix factorizations for collaborative filtering. In: IEEE International Conference on Data Mining, pp. 1025–1030. IEEE Computer Society (2010).  https://doi.org/10.1109/ICDM.2010.116
  64. 64.
    Sill, J., Takacs, G., Mackey, L., Lin, D.: Feature-weighted linear stacking. arXiv:0911.0460 [cs], http://arxiv.org/abs/0911.0460
  65. 65.
    Smyth, B., McClave, P.: Similarity vs. diversity. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 347–361. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-44593-5_25CrossRefGoogle Scholar
  66. 66.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. (2009). http://www.hindawi.com/journals/aai/2009/421425/abs/
  67. 67.
    Weng, L.T., Xu, Y., Li, Y., Nayak, R.: Improving recommendation novelty based on topic taxonomy. In: 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, pp. 115–118 (2007)Google Scholar
  68. 68.
    Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, pp. 123–130. ACM (2008).  https://doi.org/10.1145/1454008.1454030
  69. 69.
    Zhang, Y.C., Saghdha, D.Ò., Quercia, D., Jambor, T.: Auralist: introducing serendipity into music recommendation. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM 2012, pp. 13–22. ACM (2012).  https://doi.org/10.1145/2124295.2124300
  70. 70.
    Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, WWW 2005, pp. 22–32. ACM (2005).  https://doi.org/10.1145/1060745.1060754

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Authors and Affiliations

  1. 1.GroupLens Research, Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.People and Information Research Team (PIReT), Department of Computer ScienceBoise State UniversityBoiseUSA

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