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A Comprehensive Survey of Neighborhood-Based Recommendation Methods

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Recommender Systems Handbook

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Abstract

Among collaborative recommendation approaches, methods based on nearest-neighbors still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter presents a comprehensive survey of neighborhood-based methods for the item recommendation problem. In particular, the main benefits of such methods, as well as their principal characteristics, are described. Furthermore, this document addresses the essential decisions that are required while implementing a neighborhood-based recommender system, and gives practical information on how to make such decisions. Finally, the problems of sparsity and limited coverage, often observed in large commercial recommender systems, are discussed, and some solutions to overcome these problems are presented.

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Notes

  1. 1.

    The distribution of ratings in real-life data is normally skewed, i.e. most ratings are given to a small proportion of items.

  2. 2.

    http://www.grouplens.org/.

References

  1. Adams, R.P., Dahl, G.E., Murray, I.: Incorporating side information into probabilistic matrix factorization using Gaussian processes. In: P. Grünwald, P. Spirtes (eds.) Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, pp. 1–9 (2010)

    Google Scholar 

  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)

    Article  Google Scholar 

  3. Agarwal, D., Chen, B.C., Long, B.: Localized factor models for multi-context recommendation. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’11, pp. 609–617. ACM, New York, NY, USA (2011). DOI http://doi.acm.org/10.1145/2020408.2020504. URL http://doi.acm.org/10.1145/2020408.2020504

  4. Balabanović, M., Shoham, Y.: Fab: Content-based, collaborative recommendation. Communications of the ACM 40(3), 66–72 (1997)

    Article  Google Scholar 

  5. Baltrunas, L., Ricci, F.: Item weighting techniques for collaborative filtering. In: Knowledge Discovery Enhanced with Semantic and Social Information, pp. 109–126. Springer (2009)

    Google Scholar 

  6. Bell, R., Koren, Y., Volinsky, C.: Modeling relationships at multiple scales to improve accuracy of large recommender systems. In: KDD ’07: Proc. of the 13th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 95–104. ACM, New York, NY, USA (2007)

    Google Scholar 

  7. Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: ICML ’98: Proc. of the 15th Int. Conf. on Machine Learning, pp. 46–54. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1998)

    Google Scholar 

  8. Billsus, D., Pazzani, M.J.: User modeling for adaptive news access. User Modeling and User-Adapted Interaction 10(2–3), 147–180 (2000)

    Article  Google Scholar 

  9. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)

    MATH  Google Scholar 

  10. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. of the 14th Annual Conf. on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann (1998)

    Google Scholar 

  11. Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 30(1–7), 107–117 (1998)

    Article  Google Scholar 

  12. Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to order things. In: NIPS ’97: Proc. of the 1997 Conf. on Advances in Neural Information Processing Systems, pp. 451–457. MIT Press, Cambridge, MA, USA (1998)

    Google Scholar 

  13. Degemmis, M., Lops, P., Semeraro, G.: A content-collaborative recommender that exploits wordnet-based user profiles for neighborhood formation. User Modeling and User-Adapted Interaction 17(3), 217–255 (2007)

    Article  Google Scholar 

  14. Delgado, J., Ishii, N.: Memory-based weighted majority prediction for recommender systems. In: Proc. of the ACM SIGIR’99 Workshop on Recommender Systems (1999)

    Google Scholar 

  15. Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Transaction on Information Systems 22(1), 143–177 (2004)

    Article  Google Scholar 

  16. Fouss, F., Renders, J.M., Pirotte, A., Saerens, M.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering 19(3), 355–369 (2007)

    Article  Google Scholar 

  17. Fouss, F., Yen, L., Pirotte, A., Saerens, M.: An experimental investigation of graph kernels on a collaborative recommendation task. In: ICDM ’06: Proc. of the 6th Int. Conf. on Data Mining, pp. 863–868. IEEE Computer Society, Washington, DC, USA (2006)

    Google Scholar 

  18. Freund, Y., Iyer, R.D., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. In: ICML ’98: Proc. of the 15th Int. Conf. on Machine Learning, pp. 170–178. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1998)

    Google Scholar 

  19. Gobel, F., Jagers, A.: Random walks on graphs. Stochastic Processes and Their Applications 2, 311–336 (1974)

    Article  MathSciNet  Google Scholar 

  20. Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4(2), 133–151 (2001)

    Article  MATH  Google Scholar 

  21. Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B., Herlocker, J., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: AAAI ’99/IAAI ’99: Proc. of the 16th National Conf. on Artificial Intelligence, pp. 439–446. American Association for Artificial Intelligence, Menlo Park, CA, USA (1999)

    Google Scholar 

  22. Gori, M., Pucci, A.: Itemrank: a random-walk based scoring algorithm for recommender engines. In: Proc. of the 2007 IJCAI Conf., pp. 2766–2771 (2007)

    Google Scholar 

  23. Grcar, M., Fortuna, B., Mladenic, D., Grobelnik, M.: k-NN versus SVM in the collaborative filtering framework. Data Science and Classification pp. 251–260 (2006). URL http://db.cs.ualberta.ca/webkdd05/proc/paper25-mladenic.pdf

  24. Herlocker, J., Konstan, J.A., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr. 5(4), 287–310 (2002)

    Article  Google Scholar 

  25. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR ’99: Proc. of the 22nd Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 230–237. ACM, New York, NY, USA (1999)

    Google Scholar 

  26. 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)

    Article  Google Scholar 

  27. Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: CHI ’95: Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, pp. 194–201. ACM Press/Addison-Wesley Publishing Co., New York, NY, USA (1995)

    Google Scholar 

  28. Hofmann, T.: Collaborative filtering via Gaussian probabilistic latent semantic analysis. In: SIGIR ’03: Proc. of the 26th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 259–266. ACM, New York, NY, USA (2003)

    Google Scholar 

  29. Howe, A.E., Forbes, R.D.: Re-considering neighborhood-based collaborative filtering parameters in the context of new data. In: CIKM ’08: Proceeding of the 17th ACM conference on Information and knowledge management, pp. 1481–1482. ACM, New York, NY, USA (2008)

    Google Scholar 

  30. Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems 22(1), 116–142 (2004)

    Article  Google Scholar 

  31. Jin, R., Chai, J.Y., Si, L.: An automatic weighting scheme for collaborative filtering. In: SIGIR ’04: Proc. of the 27th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 337–344. ACM, New York, NY, USA (2004)

    Google Scholar 

  32. Jin, R., Si, L., Zhai, C.: Preference-based graphic models for collaborative filtering. In: Proc. of the 19th Annual Conf. on Uncertainty in Artificial Intelligence (UAI-03), pp. 329–33. Morgan Kaufmann, San Francisco, CA (2003)

    Google Scholar 

  33. Jin, R., Si, L., Zhai, C., Callan, J.: Collaborative filtering with decoupled models for preferences and ratings. In: CIKM ’03: Proc. of the 12th Int. Conf. on Information and Knowledge Management, pp. 309–316. ACM, New York, NY, USA (2003)

    Google Scholar 

  34. Kabbur, S., Ning, X., Karypis, G.: Fism: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’13, pp. 659–667. ACM, New York, NY, USA (2013). DOI 10.1145/2487575.2487589. URL http://doi.acm.org/10.1145/2487575.2487589

  35. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

    Article  MATH  Google Scholar 

  36. Kendall, M., Gibbons, J.D.: Rank Correlation Methods, 5 edn. Charles Griffin (1990)

    Google Scholar 

  37. Koenigstein, N., Koren, Y.: Towards scalable and accurate item-oriented recommendations. In: Proceedings of the 7th ACM conference on Recommender systems, RecSys ’13, pp. 419–422. ACM, New York, NY, USA (2013). DOI 10.1145/2507157.2507208. URL http://doi.acm.org/10.1145/2507157.2507208

  38. Kondor, R.I., Lafferty, J.D.: Diffusion kernels on graphs and other discrete input spaces. In: ICML ’02: Proc. of the Nineteenth Int. Conf. on Machine Learning, pp. 315–322. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2002)

    Google Scholar 

  39. Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: applying collaborative filtering to usenet news. Communications of the ACM 40(3), 77–87 (1997)

    Article  Google Scholar 

  40. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD’08: Proceeding of the 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 426–434. ACM, New York, NY, USA (2008)

    Google Scholar 

  41. Kunegis, J., Lommatzsch, A., Bauckhage, C.: Alternative similarity functions for graph kernels. In: Proc. of the Int. Conf. on Pattern Recognition (2008)

    Book  Google Scholar 

  42. Lang, K.: News Weeder: Learning to filter netnews. In: Proc. of the 12th Int. Conf. on Machine Learning, pp. 331–339. Morgan Kaufmann publishers Inc.: San Mateo, CA, USA (1995)

    Google Scholar 

  43. Li, J., Zaiane, O.R.: Combining usage, content, and structure data to improve Web site recommendation. In: Proc. of the 5th Int. Conf. on Electronic Commerce and Web Technologies (EC-Web) (2004)

    Google Scholar 

  44. Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)

    Google Scholar 

  45. Luo, H., Niu, C., Shen, R., Ullrich, C.: A collaborative filtering framework based on both local user similarity and global user similarity. Machine Learning 72(3), 231–245 (2008)

    Article  Google Scholar 

  46. Ma, H., King, I., Lyu, M.R.: Effective missing data prediction for collaborative filtering. In: SIGIR ’07: Proc. of the 30th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 39–46. ACM, New York, NY, USA (2007)

    Google Scholar 

  47. Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: 18th National Conf. on Artificial intelligence, pp. 187–192. American Association for Artificial Intelligence, Menlo Park, CA, USA (2002)

    Google Scholar 

  48. Nakamura, A., Abe, N.: Collaborative filtering using weighted majority prediction algorithms. In: ICML ’98: Proc. of the 15th Int. Conf. on Machine Learning, pp. 395–403. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1998)

    Google Scholar 

  49. Natarajan, N., Shin, D., Dhillon, I.S.: Which app will you use next?: collaborative filtering with interactional context. In: Proceedings of the 7th ACM conference on Recommender systems, RecSys ’13, pp. 201–208. ACM, New York, NY, USA (2013). DOI 10.1145/2507157.2507186. URL http://doi.acm.org/10.1145/2507157.2507186

  50. Ning, X., Karypis, G.: Slim: Sparse linear methods for top-n recommender systems. In: Proceedings of 11th IEEE International Conference on Data Mining, pp. 497–506 (2011)

    Google Scholar 

  51. Ning, X., Karypis, G.: Sparse linear methods with side information for top-n recommendations. In: Proceedings of the sixth ACM conference on Recommender systems, RecSys ’12, pp. 155–162. ACM, New York, NY, USA (2012). DOI 10.1145/2365952.2365983. URL http://doi.acm.org/10.1145/2365952.2365983

  52. Norris, J.R.: Markov Chains, 1 edn. Cambridge University Press, Cambridge (1999)

    Google Scholar 

  53. Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of the KDD Cup and Workshop (2007)

    Google Scholar 

  54. Pazzani, M., Billsus, D.: Learning and revising user profiles: The identification of interesting Web sites. Machine Learning 27(3), 313–331 (1997)

    Article  Google Scholar 

  55. Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review 13(5–6), 393–408 (1999)

    Article  Google Scholar 

  56. Rendle, S., Freudenthaler, C., Gantner, Z., Lars, S.T.: Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI ’09, pp. 452–461. AUAI Press, Arlington, Virginia, United States (2009)

    Google Scholar 

  57. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An open architecture for collaborative filtering of netnews. In: CSCW ’94: Proc. of the 1994 ACM Conf. on Computer Supported Cooperative Work, pp. 175–186. ACM, New York, NY, USA (1994)

    Google Scholar 

  58. Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted Boltzmann machines for collaborative filtering. In: ICML ’07: Proceedings of the 24th international conference on Machine learning, pp. 791–798. ACM, New York, NY, USA (2007)

    Google Scholar 

  59. Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW ’01: Proc. of the 10th Int. Conf. on World Wide Web, pp. 285–295. ACM, New York, NY, USA (2001)

    Google Scholar 

  60. Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.T.: Application of dimensionality reduction in recommender systems – A case study. In: ACM WebKDD Workshop (2000)

    Google Scholar 

  61. Sarwar, B.M., Konstan, J.A., Borchers, A., Herlocker, J., Miller, B., Riedl, J.: Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In: CSCW ’98: Proc. of the 1998 ACM Conf. on Computer Supported Cooperative Work, pp. 345–354. ACM, New York, NY, USA (1998)

    Google Scholar 

  62. Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: SIGIR ’02: Proc. of the 25th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 253–260. ACM, New York, NY, USA (2002)

    Google Scholar 

  63. Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating “word of mouth”. In: CHI ’95: Proc. of the SIGCHI Conf. on Human factors in Computing Systems, pp. 210–217. ACM Press/Addison-Wesley Publishing Co., New York, NY, USA (1995)

    Google Scholar 

  64. Shi, J., Malik, J.: Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on 22(8), 888–905 (2000)

    Article  Google Scholar 

  65. Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceeding of the 14th ACM International Conference on Knowledge Discovery and Data Mining, pp. 650–658 (2008). DOI http://doi.acm.org/10.1145/1401890.1401969. URL http://doi.acm.org/10.1145/1401890.1401969

  66. Soboroff, I.M., Nicholas, C.K.: Combining content and collaboration in text filtering. In: Proc. of the IJCAI’99 Workshop on Machine Learning for Information Filtering, pp. 86–91 (1999)

    Google Scholar 

  67. Takács, G., Pilászy, I., Németh, B., Tikk, D.: Major components of the gravity recommendation system. SIGKDD Exploration Newsletter 9(2), 80–83 (2007)

    Article  Google Scholar 

  68. Takács, G., Pilászy, I., Németh, B., Tikk, D.: Investigation of various matrix factorization methods for large recommender systems. In: Proc. of the 2nd KDD Workshop on Large Scale Recommender Systems and the Netflix Prize Competition (2008)

    Google Scholar 

  69. Takács, G., Pilászy, I., Németh, B., Tikk, D.: Scalable collaborative filtering approaches for large recommender systems. Journal of Machine Learning Research (Special Topic on Mining and Learning with Graphs and Relations) 10, 623–656 (2009)

    Google Scholar 

  70. Terveen, L., Hill, W., Amento, B., McDonald, D., Creter, J.: PHOAKS: a system for sharing recommendations. Communications of the ACM 40(3), 59–62 (1997)

    Article  Google Scholar 

  71. Yoo, J., Choi, S.: Weighted nonnegative matrix co-tri-factorization for collaborative prediction. In: Z.H. Zhou, T. Washio (eds.) Advances in Machine Learning, Lecture Notes in Computer Science, vol. 5828, pp. 396–411. Springer Berlin / Heidelberg (2009)

    Google Scholar 

  72. Zitnick, C.L., Kanade, T.: Maximum entropy for collaborative filtering. In: AUAI ’04: Proc. of the 20th Conf. on Uncertainty in Artificial Intelligence, pp. 636–643. AUAI Press, Arlington, Virginia, United States (2004)

    Google Scholar 

  73. Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. Journal Of The Royal Statistical Society Series B 67(2), 301–320 (2005)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Department, Purdue University, West Lafayette, IN, USA

    Xia Ning

  2. Software Engineering and IT Department, École de Technologie Supérieure, Montreal, QC, Canada

    Christian Desrosiers

  3. Computer Science and Engineering Department, University of Minnesota, Minneapolis, MN, USA

    George Karypis

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  1. Xia Ning
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  2. Christian Desrosiers
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Correspondence to Christian Desrosiers .

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Editors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

    Francesco Ricci

  2. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Ning, X., Desrosiers, C., Karypis, G. (2015). A Comprehensive Survey of Neighborhood-Based Recommendation Methods. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_2

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