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A Comprehensive Survey of Neighborhood-based Recommendation Methods

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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 a few solutions to overcome these problems are presented.

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References

  1. 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 

  2. 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: KDD ’99: Proc. of the 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 201–212. ACM, New York, NY, USA (1999)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  4. 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)

    Chapter  Google Scholar 

  5. Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: ICDM ’07: Proc. of the 2007 Seventh IEEE Int. Conf. on Data Mining, pp. 43–52. IEEE Computer Society, Washington, DC, USA (2007)

    Chapter  Google Scholar 

  6. Billsus, D., Brunk, C.A., Evans, C., Gladish, B., Pazzani, M.: Adaptive interfaces for ubiquitous web access. Communications of the ACM 45(5), 34–38 (2002)

    Article  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)

    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. Buckley, C., Salton, G.: Optimization of relevance feedback weights. In: SIGIR ’95: Proc. of the 18th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 351–357. ACM, New York, NY, USA (1995)

    Chapter  Google Scholar 

  13. 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 

  14. Crestani, F., Lee, P.L.: Searching the Web by constrained spreading activation. Information Processing and Management 36(4), 585–605 (2000)

    Article  Google Scholar 

  15. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society for Information Science 41, 391–407 (1990)

    Article  Google Scholar 

  16. 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 

  17. 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 

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

    Article  Google Scholar 

  19. 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 

  20. 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 

  21. 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 

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

    Article  MathSciNet  Google Scholar 

  23. 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 

  24. Golub, G.H., Van Loan, C.F.: Matrix computations (3rd ed.). Johns Hopkins University Press (1996)

    Google Scholar 

  25. 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 

  26. 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 

  27. 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

  28. 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 

  29. 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)

    Chapter  Google Scholar 

  30. 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 

  31. 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)

    Chapter  Google Scholar 

  32. 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)

    Chapter  Google Scholar 

  33. 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)

    Chapter  Google Scholar 

  34. 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 

  35. 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 

  36. 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 

  37. 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)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  40. 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 

  41. 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 

  42. 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)

    Chapter  Google Scholar 

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

    Google Scholar 

  44. 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 

  45. Last.fm: Music recommendation service (2009). http://www.last.fm

  46. 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 

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

    Article  Google Scholar 

  48. 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)

    Google Scholar 

  49. 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)

    Chapter  Google Scholar 

  50. 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 

  51. Miller, B.N., Albert, I., Lam, S.K., Konstan, J.A., Riedl, J.: Movielens unplugged: experiences with an occasionally connected recommender system. In: IUI ’03: Proc. of the 8th Int. Conf. on Intelligent User Interfaces, pp. 263–266. ACM, New York, NY, USA (2003)

    Chapter  Google Scholar 

  52. Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Discovery and evaluation of aggregate usage profiles for Web personalization. Data Mining and Knowledge Discovery 6(1), 61–82 (2002)

    Article  MathSciNet  Google Scholar 

  53. Mooney, R.J.: Content-based book recommending using learning for text categorization. In: Proc. of the Fifth ACM Conf. on Digital Libraries, pp. 195–204. ACM Press (2000)

    Google Scholar 

  54. 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 

  55. Netflix: Online movie rental service (2009). http://www.netflix.com

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

    Google Scholar 

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

    Google Scholar 

  58. 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 

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

    Article  Google Scholar 

  60. 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)

    Chapter  Google Scholar 

  61. Rich, E.: User modeling via stereotypes. Cognitive Science 3(4), 329–354 (1979)

    Google Scholar 

  62. Rissanen, J.: Modeling by shortest data description. Automatica 14, 465–471 (1978)

    Article  MATH  Google Scholar 

  63. Rocchio, J.: Relevance Feedback in Information Retrieval. Prentice Hall, Englewood, Cliffs, New Jersey (1971)

    Google Scholar 

  64. 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)

    Chapter  Google Scholar 

  65. Salton, G. (ed.): Automatic text processing. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA (1988)

    Google Scholar 

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

    Google Scholar 

  67. 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 

  68. 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)

    Chapter  Google Scholar 

  69. 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)

    Chapter  Google Scholar 

  70. 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) 144 Christian Desrosiers and George Karypis

    Chapter  Google Scholar 

  71. Sheth, B., Maes, P.: Evolving agents for personalized information filtering. In: Proc. of the 9th Conf. on Artificial Intelligence for Applications, pp. 345–352 (1993)

    Google Scholar 

  72. 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 

  73. 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 

  74. 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 

  75. 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 

  76. 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 

  77. Zhang, Y., Callan, J., Minka, T.: Novelty and redundancy detection in adaptive filtering. In: SIGIR ’02: Proc. of the 25th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 81–88. ACM, New York, NY, USA (2002)

    Chapter  Google Scholar 

  78. 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 

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Author information

Authors and Affiliations

  1. Software Engineering and IT dep., École de Technologie Supérieure, Montreal, Canada

    Christian Desrosiers

  2. Computer Science & Engineering dep., University of Minnesota, Minneapolis, USA

    George Karypis

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  1. Christian Desrosiers
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  2. George Karypis
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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, Piazza Domenicani 3, Bolzano, 39100, Italy

    Francesco Ricci

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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

    Bracha Shapira

  4. School of Communication,, Information & Library Studies, Rutgers University, Huntington Street 4, New Brunswick, 08901-1071, New Jersey, USA

    Paul B. Kantor

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Desrosiers, C., Karypis, G. (2011). A Comprehensive Survey of Neighborhood-based Recommendation Methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85820-3_4

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