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Matching Recommendation Technologies and Domains

  • Robin Burke
  • Maryam Ramezani
Chapter

Abstract

Recommender systems form an extremely diverse body of technologies and approaches. The chapter aims to assist researchers and developers to identify the recommendation technologies that are most likely to be applicable to different domains of recommendation. Unlike other taxonomies of recommender systems, our approach is centered on the question of knowledge: what knowledge does a recommender system need in order to function, and where does that knowledge come from? Different recommendation domains (books vs condominiums, for example) provide different opportunities for the gathering and application of knowledge. These considerations give rise to a mapping between domain characteristics and recommendation technologies.

Keywords

Content Knowledge Recommender System Social Knowledge Knowledge Source Item Feature 
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.

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Notes

Acknowledgements

This article is based on research performed by Ms. Ramezani at IBM Watson Research Center during the summer of 2007. An abbreviated version of the article with additional authors Lawrence Bergman, Rich Thompson and Bamshad Mobasher appeared as “Selecting and Applying Recommendation Technologies” at the Workshop on Recommendation and Collaboration at the Intelligent User Interfaces conference 2008.

References

  1. 1.
    Aciar, S., Zhang, D., Simoff, S., Debenham, J.: Informed recommender agent: Utilizing consumer product reviews through text mining. In: WI-IATW ’06: Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology, pp. 37–40. IEEE Computer Society, Hong Kong (2006).Google Scholar
  2. 2.
    Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. 23(1), 103–145 (2005).CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Anand, S.S., Mobasher, B.: Introduction to intelligent techniques for web personalization. ACM Trans. Interet Technol. 7(4), 18 (2007).CrossRefGoogle Scholar
  5. 5.
    Berkovsky, S., Aroyo, L., Heckmann, D., Houben, G.J., Kr¨oner, A., Kuflik, T., Ricci, F.: Providing context-aware personalization through cross-context reasoning of user modeling data. In: S. Berkovsky, K. Cheverst, P. Dolog, D. Heckmann, T. Kuflik, P. Mylonas, J. Picault, J. Vassileva (eds.) UbiDeUM’2007 - International Workshop on Ubiquitous and Decentralized User Modeling, at User Modeling 2007, 11th International Conference, UM 2007, Corfu, Greece, June 26, 2007, Proceedings (2007).Google Scholar
  6. 6.
    Billsus, D., Pazzani, M.J.: User modeling for adaptive news access. User Modeling and User- Adapted Interaction 10(2-3), 147–180 (2000)CrossRefGoogle Scholar
  7. 7.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artiffcial Intelligence, pp. 43–52 (1998).Google Scholar
  8. 8.
    Budzik, J., Hammond, K., Birnbaum, L.: Information access in context. Knowledge based systems 14(1-2), 37–53 (2001)CrossRefGoogle Scholar
  9. 9.
    Buono, P., Costabile, M.F., Guida, T., Piccinno, A.: Integrating user data and collaborative filtering in a web recommendation system. In: Lecture Notes in Computer Science: Hypermedia: Openness, Structural Awareness, and Adaptivity, pp. 192–196. SpringerLink (2002)Google Scholar
  10. 10.
    Burke, R.: Knowledge-based recommender systems. In: Encyclopedia of Library and Information Systems. Marcel Dekker (2000)Google Scholar
  11. 11.
    Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User- Adapted Interaction 12(4), 331–370 (2002)MATHCrossRefGoogle Scholar
  12. 12.
    Burke, R.: Interactive critiquing for catalog navigation in e-commerce. Artif. Intell. Rev. 18(3-4), 245–267 (2002)CrossRefGoogle Scholar
  13. 13.
    Castro-Herrera, C., Duan, C., Cleland-Huang, J., Mobasher, B.: Using data mining and recommender systems to facilitate large-scale, open, and inclusive requirements elicitation processes. In: RE ’08: Proceedings of the 2008 16th IEEE International Requirements Engineering Conference, pp. 165–168. IEEE Computer Society, Washington, DC, USA (2008).CrossRefGoogle Scholar
  14. 14.
    Chen, H.C., Chen, A.L.P.: A music recommendation system based on music data grouping and user interests. In: CIKM ’01: Proceedings of the tenth international conference on Information and knowledge management, pp. 231–238. ACM (2001).Google Scholar
  15. 15.
    Chen, L., Pu, P.: Survey of preference elicitation methods. Tech. rep., Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Switzerland (2004).Google Scholar
  16. 16.
    Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: WWW ’07: Proceedings of the 16th international conference on World Wide Web, pp. 271–280. ACM, New York, NY, USA (2007).CrossRefGoogle Scholar
  17. 17.
    Felfernig, A.: Koba4ms: Selling complex products and services using knowledge-based recommender technologies. In: CEC ’05: Proceedings of the Seventh IEEE International Conference on E-Commerce Technology (CEC’05), pp. 92–100. IEEE Computer Society (2005).Google Scholar
  18. 18.
    Felfernig, A., Burke, R.: Constraint-based recommender systems: technologies and research issues. In: ICEC ’08: Proceedings of the 10th international conference on Electronic commerce, pp. 1–10. ACM, New York, NY, USA (2008).Google Scholar
  19. 19.
    Felfernig, A., Kiener, A.: Knowledge-based interactive selling of financial services with fsadvisor. In: Proceedings of the National Conference on Artificial Intelligence, pp. 1475–1482 (2005)Google Scholar
  20. 20.
    Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Inf. Retr. 4(2), 133–151 (2001).MATHCrossRefGoogle Scholar
  21. 21.
    Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B.M., Herlocker, J.L., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: AAAI:Conference on Artificial Intelligence, pp. 439–446 (1999).Google Scholar
  22. 22.
    Hauptmann, A.G.: Integrating and using large databases of text, images, video, and audio. Intelligent Systems and Their Applications, IEEE 14(5), 34 –35 (1999)Google Scholar
  23. 23.
    Hayes, C., Avesani, P., Veeramachaneni, S.: An analysis of the use of tags in a blog recommender system. In: M.M. Veloso (ed.) IJCAI-07, the International Joint Conference on Artificial Intelligence, pp. 2772–2777 (2007)Google Scholar
  24. 24.
    Hayes, C., Cunningham, P.: Smart radio: Building music radio on the fly. In: Proceedings of Expert Systems 2000, Cambridge, UK (2000)Google Scholar
  25. 25.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR ’99: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 230–237. ACM Press, New York, NY, USA (1999).CrossRefGoogle Scholar
  26. 26.
    Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: CSCW ’00: Proceedings of the 2000 ACM conference on Computer supported cooperative work, pp. 241–250. ACM Press (2000).Google Scholar
  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).CrossRefGoogle Scholar
  28. 28.
    Hijikata, Y., Iwahama, K., Nishida, S.: Content-based music filtering system with editable user profile. In: SAC ’06: Proceedings of the 2006 ACM symposium on Applied computing, pp. 1050–1057. ACM (2006).Google Scholar
  29. 29.
    Holmes, R., Walker, R.J., Murphy, G.C.: Approximate structural context matching: An approach to recommend relevant examples. IEEE Transactions on Software Engineering 32(12), 952–970 (2006).Google Scholar
  30. 30.
    Kosala, R., Blockeel, H.:Web mining research: a survey. SIGKDD Explor. Newsl. 2(1), 1–15 (2000).CrossRefGoogle Scholar
  31. 31.
    Kovacs, A.I., Ueno, H.: Recommending in context: A spreading activation model that is independent of the type of recommender system and its contents. In: G. Uchyigit (ed.) Proceedings of Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces In conjunction with AH 2006:International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems. Dublin, Ireland (2006)Google Scholar
  32. 32.
    Krulwich, B.: Lifestyle finder: Intelligent user profiling using large-scale demographic data. Artificial Intelligence Magazine 18(2) (1997)Google Scholar
  33. 33.
    Kurucz, M., Benczúr, A.A., Kiss, T., Nagy, I., Szabó, A., Torma, B.: Kdd cup 2007 task 1 winner report. SIGKDD Explor. Newsl. 9(2), 53–56 (2007).Google Scholar
  34. 34.
    Lang, K.: Newsweeder: Learning to filter netnews. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 331–339 (1995)Google Scholar
  35. 35.
    Lee, D.H., Brusilovsky, P.: Fighting information overflow with personalized comprehensive information access: A proactive job recommender. In: ICAS ’07: Proceedings of the Third International Conference on Autonomic and Autonomous Systems, p. 21. IEEE Computer Society, Washington, DC, USA (2007).Google Scholar
  36. 36.
    Li, J., Zaiane, O.R.: Combining usage, content, and structure data to improve web site recommendation. Lecture Notes in Computer Science : E-Commerce andWeb Technologies pp. 305–315 (2004)Google Scholar
  37. 37.
    Mahmood, T., Ricci, F., Venturini, A., Hpken, W.: Adaptive recommender systems for travel planning. In: P. O’Connor, W. Hpken, U. Gretzel (eds.) Information and Communication Technologies in Tourism 2008, pp. 1–11. Springer (2008).Google Scholar
  38. 38.
    Maidel, V., Shoval, P., Shapira, B., Taieb-Maimon, M.: Evaluation of an ontology-content based filtering method for a personalized newspaper. In: RecSys ’08: Proceedings of the 2008 ACM conference on Recommender systems, pp. 91–98. ACM (2008).Google Scholar
  39. 39.
    Malinowski, J., Keim, T., Wendt, O., Weitzel, T.: Matching people and jobs: A bilateral recommendation approach. In: HICSS ’06: Proceedings of the 39th Annual Hawaii International Conference on System Sciences, pp. 137c–137c. IEEE Computer Society (2006).Google Scholar
  40. 40.
    Markines, B., Stoilova, L., Menczer, F.: Bookmark hierarchies and collaborative recommendation. In: Proceedings of the Twenty-First AAAI Conference on Artificial Intelligence. AAAI Press (2006)Google Scholar
  41. 41.
    McSherry, D.: Explaining the pros and cons of conclusions in cbr. In: P.A.G. Calero, P. Funk (eds.) Proceedings of the European Conference on Case-Based Reasoning (ECCBR-04), pp. 317–330. Springer (2004). Madrid, SpainGoogle Scholar
  42. 42.
    McSherry, D.: Explanation in recommender systems. Artif. Intell. Rev. 24(2), 179–197 (2005) 43. McSherry, D., Aha, D.W.: Mixed-initiative relaxation of constraints in critiquing dialogues. In: ICCBR ’07: Proceedings of the 7th international conference on Case-Based Reasoning, vol. 4626, pp. 107–121 (2007)Google Scholar
  43. 44.
    Mladenic, D., Grobelnik, M.: Feature selection for unbalanced class distribution and naive bayes. In: ICML ’99: Proceedings of the Sixteenth International Conference on Machine Learning, pp. 258–267. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) 11 Matching Recommendation Technologies and Domains 385Google Scholar
  44. 45.
    Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. Communications of the ACM 43(8), 142–151 (2000).Google Scholar
  45. 46.
    Montaner, M., López, B., Rosa, J.L.D.L.: A taxonomy of recommender agents on the internet. Artif. Intell. Rev. 19(4), 285–330 (2003).CrossRefGoogle Scholar
  46. 47.
    Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: DL ’00: Proceedings of the fifth ACM conference on Digital libraries, pp. 195–204. ACM Press (2000).Google Scholar
  47. 48.
    Moskovitch, R., Elovici Y., Rokach L., Detection of unknown computer worms based on behavioral classification of the host, Computational Statistics and Data Analysis, 52(9):4544–4566 (2008)MATHMathSciNetGoogle Scholar
  48. 49.
    Niwa, S., Doi, T., Honiden, S.: Web page recommender system based on folksonomy mining for itng 06. In: Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on, pp. 388–393 (2006).Google Scholar
  49. 50.
    Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proc. of the of the KDD Cup and Workshop 2007 (KDD 2007) (2007)Google Scholar
  50. 51.
    Pazzani, M., Billsus, D.: Learning and revising user profiles: The identification ofinteresting web sites. Machine Learning: Special issue on multistrategy learning 27(3), 313–331 (1997).Google Scholar
  51. 52.
    Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5-6), 393–408 (1999)CrossRefGoogle Scholar
  52. 53.
    Rack, C., Arbanowski, S., Steglich, S.: A Generic Multipurpose recommender System for Contextual Recommendations, pp. 445–450. IEEE Computer Society,Washington, DC, USA (2007).Google Scholar
  53. 54.
    Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  54. 55.
    Ricci, F.: Travel recommender systems. In: IEEE Intelligent Systems, pp. 55–57 (2002)Google Scholar
  55. 56.
    Rosset, S., Perlich, C., Liu, Y.: Making the most of your data: Kdd cup 2007 ”how many ratings” winner’s report. SIGKDD Explor. Newsl. 9(2), 66–69 (2007).CrossRefGoogle Scholar
  56. 57.
    Roth-Berghofer, T.R.: Explanations and case-based reasoning: Foundational issues. In: AAdvances in Case-Based Reasoning, pp. 389–403. Springer Verlag (2004)Google Scholar
  57. 58.
    Salam, M., Reilly, J., McGinty, L., Smyth, B.: Knowledge discovery from user preferences in conversational recommendation. In: Knowledge Discovery in Databases: PKDD 2005, pp. 228–239. Springer Berlin / Heidelberg (2005)Google Scholar
  58. 59.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In:WWW’01: Proceedings of the 10th international conference onWorld Wide Web, pp. 285–295. ACM (2001).Google Scholar
  59. 60.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender systems-a case study. In: Proceedings of ACM WebKDD Workshop (2000)Google Scholar
  60. 61.
    Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Mining and Knowledge Discovery 5(1-2), 115–153 (2001)MATHCrossRefGoogle Scholar
  61. 62.
    van Setten, M., Pokraev, S., Koolwaaij, J.: Context-aware recommendations in the mobile tourist application compass. In: W. Nejdl, P. De Bra (eds.) Adaptive Hypermedia 2004, pp. 235–244. Springer Verlag (2004).Google Scholar
  62. 63.
    Smyth, B., Balfe, E., Freyne, J., Briggs, P., Coyle, M., Boydell, O.: Exploiting query repetition and regularity in an adaptive community-based web search engine. User Modeling and User- Adapted Interaction 14(5), 383–423 (2005).CrossRefGoogle Scholar
  63. 64.
    Takács, G., Pilászy, I., Németh, B., Tikk, D.: On the gravity recommendation system. In: Proc. of the of the KDD Cup and Workshop 2007 (KDD 2007), pp. 22–30 (2007)Google Scholar
  64. 65.
    Tartakovski, A., Schaaf, M., Bergmann, R.: Retrieval and configuration of life insurance policies. In: Lecture Notes in Computer Science, Case-Based Reasoning Research and Development, pp. 552–565. Springer Berlin / Heidelberg (2005)Google Scholar
  65. 66.
    Ungar, L., Foster, D., Andre, E., Wars, S., Wars, F.S., Wars, D.S., Whispers, J.H.: Clustering methods for collaborative filtering. In: Workshop on Recommender Systems at the 15th National Conference on Artificial Intelligence. AAAI Press (1998) 386 Robin Burke and Maryam RamezaniGoogle Scholar
  66. 67.
    Viappiani, P., Pu, P., Faltings, B.: Conversational recommenders with adaptive suggestions. In: RecSys ’07: Proceedings of the 2007 ACM conference on Recommender systems, pp. 89–96. ACM (2007).Google Scholar
  67. 68.
    Wu, H., Zubair, M., Maly, K.: Harvesting social knowledge from folksonomies. In: HYPERTEXT ’06: Proceedings of the seventeenth conference on Hypertext and hypermedia, pp. 111–114. ACM Press (2006).Google Scholar
  68. 69.
    Xu, Z., Fu, Y., Mao, J., Su, D.: Towards the semantic web: Collaborative tag suggestions. In: Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006. Edinburgh, Scotland (2006)Google Scholar
  69. 70.
    Yu, Z., Zhou, X., Zhang, D., Chin, C.Y., Wang, X., Men, J.: Supporting context-aware media recommendations for smart phones. Pervasive Computing 5(3), 68–75 (2006).CrossRefGoogle Scholar
  70. 71.
    Za¨ıane, O.R., Han, J., Li, Z.N., Chee, S.H., Chiang, J.Y.: Multimediaminer: a system prototype for multimedia data mining. In: SIGMOD ’98: Proceedings of the 1998 ACM SIGMOD international conference on Management of data, pp. 581–583. ACM (1998).Google Scholar
  71. 72.
    Zanker, M.: A collaborative constraint-based meta-level recommender. In: RecSys ’08: Proceedings of the 2008 ACM conference on Recommender systems, pp. 139–146. ACM, New York, NY, USA (2008).CrossRefGoogle Scholar
  72. 73.
    Zhang, J., Pu, P.: A comparative study of compound critique generation in conversational recommender systems. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4018 NCS, pp. 234–243. Springer Verlag, Heidelberg, D-69121, Germany (2006)Google Scholar
  73. 74.
    Zhang, S.,Wang,W., Ford, J., Makedon, F., Pearlman, J.: Using singular value decomposition approximation for collaborative filtering. In: CEC ’05: Proceedings of the Seventh IEEE International Conference on E-Commerce Technology, pp. 257–264. IEEE Computer Society, Washington, DC, USA (2005).CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Center for Web IntelligenceCollege of Computing and Digital Media, De-Paul UniversityChicagoUSA

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