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Analysis and Classification of Multi-Criteria Recommender Systems

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Abstract

Recent studies have indicated that the application of Multi-Criteria Decision Making (MCDM) methods in recommender systems has yet to be systematically explored. This observation partially contradicts with the fact that in related literature, there exist several contributions describing recommender systems that engage some MCDM method. Such systems, which we refer to as multi-criteria recommender systems, have early demonstrated the potential of applying MCDM methods to facilitate recommendation, in numerous application domains. On the other hand, a comprehensive analysis of existing systems would facilitate their understanding and development. Towards this direction, this paper identifies a set of dimensions that distinguish, describe and categorize multi-criteria recommender systems, based on existing taxonomies and categorizations. These dimensions are integrated into an overall framework that is used for the analysis and classification of a sample of existing multi-criteria recommender systems. The results provide a comprehensive overview of the ways current multi-criteria recommender systems support the decision of online users.

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References

  1. 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)

    Article  Google Scholar 

  2. Adomavicius, G., Tuzhilin, A.: Multidimensional recommender systems: a data warehousing approach. In Proc. 2nd Int. Worksh. Electr. Comm. (WELCOM’01), LCCS vol. 2232, pp. 180–192. Springer, Berlin Heidelberg New York (2001)

  3. Adomavicius, G., Tuzhilin, A.: Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  4. Ardissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: Intrigue: personalised recommendation of tourist attractions for desktop and handset devices. In: Applied Artificial Intelligence Special Issue on ‘Artificial Intelligence for Cultural Heritage and Digital Libraries’ vol. 17(8–9), pp. 687–714. Taylor & Thomas, New York (2003)

  5. Ariely, D., Lynch, J.G.,Jr., Aparicio, M.: Learning by collaborative and individual-based recommendation agents. J. Consum. Psych. 14(1&2), 81–94 (2004)

    Article  Google Scholar 

  6. Balabanovic, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Comm. ACM 40(3), 66–72 (1997)

    Article  Google Scholar 

  7. Baudisch, P.: Dynamic information filtering. PhD thesis, Darmstad Technical University, GMD Research Series No. 16 (2001)

  8. Belkin, N.J.: Helping people find what they don’t know. Comm. ACM 43(8), 58–61 (2000)

    Article  Google Scholar 

  9. Belkin, N.J., Croft, W.B.: Information filtering and information retrieval: two sides of the same coin? Comm. ACM 35(12), 29–38 (1992)

    Article  Google Scholar 

  10. Bharati, P., Chaudhury, A.: An empirical investigation of decision-making satisfaction in web-based decision support systems. Dec. Supp. Syst. 1051, 1–11 (2003)

    Google Scholar 

  11. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In Proc. 14th Conf. Uncert. Art. Intell., Madison WI, USA, July. Morgan Kaufmann, San Francisco (1998)

  12. Brusilovsky, P.: Methods and techniques of adaptive hypermedia. User Model. User Adapt. Inter. 6(2–3), 87–129 (1996)

    Article  Google Scholar 

  13. Brusilovsky, P., Vassileva, J.: Course sequencing techniques for large-scale web based education. Int. J. Contin. Eng. Educ. Lifelong Learn. 13(1/2), 75–94 (2003)

    Google Scholar 

  14. Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User Adapt. Inter. 12, 331–370 (2002)

    Article  MATH  Google Scholar 

  15. Carenini, G.: User-specific decision-theoretic accuracy metrics for collaborative filtering. In Proc. ‘Beyond Personalization’ Worksh., Intell. User Interf. Conf., San Diego, California, USA (2005)

  16. Changchien, S.W., Lu, T.-C.: Mining association rules procedure to support on-line recommendation by customers and products fragmentation. Exp. Syst. Appls. 20, 325–335 (2001)

    Article  Google Scholar 

  17. Cheetham, W.: Global grade selector: a recommender system for supporting the sale of plastic resin. Tech. Inf. Series, GE Global Research, TR. 2003GRC261, December. Springer, Berlin Heidelberg New York (2003)

  18. Cho, Y.H., Kim, J.K.: Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce. Exp. Syst. Appl. 26, 233–246 (2004)

    Article  Google Scholar 

  19. Cho, Y.H., Kim, J.K., Kim, S.H.: A personalised recommender system based on web usage mining and decision tree induction. Exp. Syst. Appl. 23, 329–342 (2002)

    Article  Google Scholar 

  20. Choi, S.H., Cho, Y.H.: An utility range-based similar product recommendation algorithm for collaborative companies. Exp. Syst. Appl. 27, 549–557 (2004)

    Google Scholar 

  21. Claypool, M., Gokhale, A., Miranda, T.: Combining content-based and collaborative filters in an online newspaper. In Proc. ACM SIGIR Worksh. Recomm. Syst. Impl. Eval., Berkeley CA, USA (1999)

  22. Cohen, W.W., Fan, W.: Web-collaborative filtering: recommending music by crawling the web. In Proc. 9th Int. WWW Conf., Amsterdam, Netherlands. North-Holland (2000)

  23. Cunliffe, D.: Developing usable web sites—a review and model. Int. Res.: El. Netw. Appl. Policy 10(4), 295–307 (2000)

    Article  Google Scholar 

  24. De Bra, P., Houben, G.-J., Wu, H.: AHAM: a Dexter-based reference model for adaptive hypermedia. In Proc. 10th Conf. Hypertext Hypermedia, Darmstadt, Germany, pp. 147–156. ACM, New York (1999)

  25. Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)

    Article  Google Scholar 

  26. DIMACS and LAMSADE: Computer science and decision theory: applications of notions of consesus. DIMACS/LAMSADE Partnership (http://dimacs.rutgers.edu/Workshops/Lamsade/) (2004)

  27. Falle, W., Stoefler, D., Russ, C., Zanker, M., Felfernig, A.: Using knowledge-based advisor technology for improved customer satisfaction in the shoe industry. In Proc. Int. Conf. Econ. Techn. Organis. Asp. of Product Config. Syst., Technical University of Denmark, Kopenhagen (2004)

  28. Ghosh, S., Munde, M., Hernandez, K., Sen, S.: Voting for movies: the anatomy of a recommender system. In Proc. Aut. Agents, Seattle WA USA. ACM, New York (1999)

  29. Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Comm. ACM 35(12), 61–70 (1992)

    Article  Google Scholar 

  30. Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inf. Retr. J. 4(2), 133–151 (2001)

    Article  MATH  Google Scholar 

  31. Guan, S., Ngoo, C.S., Zhu, F.: Handy broker: an intelligent product-brokering agent for m-commerce applications with user preference tracking. Electr. Comm. Res. App. 1, 314–330 (2002)

    Article  Google Scholar 

  32. Ha, V., Haddawy, P.: Similarity of personal preferences: theoretical foundations and empirical analysis. Artif. Intell. 146(2), 149–173 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  33. Han, P., Xie, B., Yang, F., Shen, R.: A scalable P2P recommender system based on distributed collaborative filtering. Exp. Syst. Appl. 27, 203–210 (2004)

    Article  Google Scholar 

  34. Hanani, U., Shapira, B., Shoval, P.: Information filtering: overview of issues, research and systems. User Model. User Adapt. Inter. 11, 203–259 (2001)

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  36. 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 

  37. Herrera-Viedma, E., Pasi, G., Lopez-Herrera, A.G.: Evaluating the information quality of web sites: a qualitative methodology based on fuzzy computing with words. Res. Group Soft Comp. Intell. Inf. Syst., Univ. of Granada, TR. #SCI2S-2004-10 (2004)

  38. Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst. 22(1), 116–142, January (2004)

    Article  Google Scholar 

  39. Hung, L.-P.: A personalized recommendation system based on product taxonomy for one-to-one marketing online. Exp. Syst. Appl. 29, 383–392 (2005)

    Article  Google Scholar 

  40. Jacquet-Lagreze, E., Siskos, J.: Assessing a set of additive utility functions for multicriteria decision-making: the UTA method. Eur. J. Oper. Res. 10, 151–164 (1982)

    Article  MATH  Google Scholar 

  41. Jacquet-Lagreze, E., Siskos, Y.: Preference disaggregation: 20 years of MCDA experience. Eur. J. Oper. Res. 130, 233–245 (2001)

    Article  MATH  Google Scholar 

  42. Karacapilidis, N., Hatzieleutheriou, L.: A hybrid framework for similarity-based recommendations. Int. J. Bus. Intell. Data Min. 1(1) (2005)

  43. Karampiperis, P., Sampson, D.: Adaptive learning resources sequencing in educational hypermedia systems. Educ. Techn. Soc. 8(4), 128–147 (2005)

    Google Scholar 

  44. Keeney, R.L.: Value-focused Thinking: A Path to Creative Decisionmaking. Harvard University Press, Cambridge, MA (1992)

    Google Scholar 

  45. Kerschberg, L., Kim, W., Scime, A.: WebSifter II: A Personalizable Meta-Search Agent Based on Semantic Weighted Taxonomy Tree. In Proc. Int. Conf. Internet Comp., Las Vegas, Nevada, USA (2001)

  46. Kim, W., Kerschberg, L., Scime, A.: Learning for automatic personalization in a semantic taxonomy-based meta-search agent. Electr. Comm. Res. App. 1, 150–173 (2002)

    Article  Google Scholar 

  47. Kim, Y.S., Yum, B.-J., Kim, S.M.: Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites. Exp. Syst. Appl. 28, 381–393 (2005)

    Article  Google Scholar 

  48. Kleinberg, J., Sandler, M.: Convergent Algorithms for Collaborative Filtering. In Proc. ACM E-Comm. Conf., San Diego California, USA. ACM, New York (2003)

  49. Konstan, J.A.: Introduction to recommender systems: algorithms and evaluation. ACM Trans. Inf. Syst. 22(1), 1–4 (2004)

    Article  Google Scholar 

  50. Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: Grouplens: applying collaborative filtering to usenet news. Comm. ACM 40(3), 77–87, March (1997)

    Article  Google Scholar 

  51. Kwon, O.B.: I know what you need to buy: context-aware multimedia-based recommendation system. Exp. Syst. Appl. 25, 387–400 (2003)

    Article  Google Scholar 

  52. Lee, W.-P.: Towards agent-based decision making in the electronic marketplace: interactive recommendation and automated negotiation. Exp. Syst. Appl. 27, 665–679 (2004)

    Article  Google Scholar 

  53. Lee, W.-P., Yang, T.-H.: Personalizing information appliances: a multi-agent framework for TV programme recommendations. Exp. Syst. Appl. 25, 331–341 (2003)

    Article  Google Scholar 

  54. Lee, W.-P., Liu, C.-H., Lu, C.-C.: Intelligent agent-based systems for personalized recommendations in internet commerce. Exp. Syst. Appl. 22, 275–284 (2002)

    Article  Google Scholar 

  55. Lee, J.-S., Jun, C.-H., Lee, J., Kim, S.: Classification-based collaborative filtering using market basket data. Exp. Syst. Appl. 29, 700–704 (2005)

    Article  Google Scholar 

  56. Li, Y., Lu, L., Xuefeng, L.: A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce. Exp. Syst. Appl. 28, 67–77 (2005)

    Article  Google Scholar 

  57. Lihua, W., Lu, L., Jing, L., Zongyong, L.: Modeling multiple interests by an improved GCS approach. Exp. Syst. Appl. 29(4), 757–767 (2005)

    Article  Google Scholar 

  58. Liu, D.-R., Shih, Y.-Y.: Integrating AHP and data mining for product recommendation based on customer lifetime value. Inf. Manag. 42, 387–400 (2005)

    Article  MATH  Google Scholar 

  59. Malone, T., Grant, K., Turbak, F., Brobst, S., Cohen, M.: Intelligent information sharing systems. Comm. ACM 30(5), 390–402 (1987)

    Article  Google Scholar 

  60. Manouselis, N., Costopoulou, C.: Designing a web-based testing tool for multi-criteria recommender systems. Eng. Lett., Sp. Iss. “Web Engineering,” in press

  61. Manouselis, N., Costopoulou, C.: Experimental analysis of design choices in a multi-attribute utility collaborative filtering system. Int. J. Patt. Recogn. Art. Int., Sp. Iss. “Personalization Techniques for Recommender Systems and Intelligent User Interfaces”, in press

  62. Manouselis, N., Costopoulou, C.: Quality in metadata: a schema for E-commerce. Online Inf. Review, Sp. Iss. “Advances in Digital Information Services and Metadata Research” 30(3), 217–237 (2006)

  63. Manouselis, N., Costopoulou, C.: Towards a design process for intelligent product recommendation services in e-markets. In: Zha, X. (ed.) Artif. Intell. Integr. Inf. Syst.: Emerg. Techn. App., Hershey, PA: Idea Group Publishing, 398–417 (2007)

  64. Manouselis, N., Sampson, D.: A multi-criteria model to support automatic recommendation of e-learning quality approaches. In Proc. 16th World Conf. Educ. Multim. Hyperm. Telecomm., Lugano, Switzerland. AACE (2004)

  65. Martin-Guerrero, J.D., Palomares, A., Balaguer-Ballester, E., Soria-Olivas, E., Gomez-Sanchis, J., Soriano-Asensi, A.: Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms. Exp. Syst. Appl. 30(2), 299–312 (2006)

    Article  Google Scholar 

  66. Middleton, S.E., Shadbolt, N.R., Roure, D.C.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. 22(1), 54–88 (2004)

    Article  Google Scholar 

  67. Miller, B.N., Konstan, J.A., Riedl, J.: PocketLens: toward a personal recommender system. ACM Trans. Inf. Syst. 22(3), 437–476 (2004)

    Article  Google Scholar 

  68. Min, S.-H., Han, I.: Detection of the customer time-invariant pattern for improving recommender systems. Exp. Syst. Appl. 28, 189–199 (2005)

    Article  Google Scholar 

  69. Minio, M., Tasso, C.: User modeling for information filtering on internet services: exploiting an extended version of the UMT shell. In Proc. Work. User Mod. Inf. Filt. WWW, User Mod. Conference (UM‘96), Kailua-Koua, Hawaii (1996)

  70. Mirza, B.J., Keller, B.J., Ramakrishnan, N.: Studying recommendation algorithms by graph analysis. J. Intell. Inf. Syst. 20(2), 131–160, March (2003)

    Article  Google Scholar 

  71. Montaner, M., Lopez, B., de la Rosa, J.L.: A taxonomy of recommender agents on the internet. Artif. Intell. Rev. 19, 285–330 (2003)

    Article  Google Scholar 

  72. Montaner, M., Lopez, B., de la Rosa, J.L.: Opinion-based filtering through trust. In Proc. 6th Int. Worksh. Cooper. Inf. Agents VI, LNCS vol. 2446, pp. 164–178, Springer, Berlin Heidelberg New York (2002)

  73. Mukherjee, R., Dutta, P.S., Jonsdottir, G., Sen, S.: MOVIES2GO—an online voting based movie recommender system. In Proc. ACM AGENTS, Montreal Quebec Canada, May. ACM, New York (2001)

  74. Nguyen, H., Haddawy, P.: DIVA: applying decision theory to collaborative filtering. In Proc. AAAI Worksh. Recomm. Syst., Madison, WI, July (1998)

  75. Nguyen, H., Haddawy, P.: The decision-theoretic video advisor. In Proc. 15th Conf. Uncert. Artif. Intell., Stockholm, Sweden, pp. 494–501. AAAI, Madison, WI (1999)

  76. Noh, S.: Implementing purchasing assistant using personal profile. In Proc. IADIS Int. Conf. App. Comp., Lisbon, Portugal, March. Kluwer, The Netherlands (2004)

  77. Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27, 313–331 (1997)

    Article  Google Scholar 

  78. Pennock, D.M., Horvitz, E.: Analysis of the axiomatic foundations of collaborative filtering. In Proc. AAAI Worksh. Artif. Intell. Electr. Comm., Orlando, Florida, July. AAAI (1999)

  79. Perny, P., Zucker, J.-D.: Collaborative filtering methods based on fuzzy preference relations. In Proc. EUROFUSE-SIC, pp. 279–285. Kluwer, The Netherlands (1999)

  80. Perny, P., Zucker, J.-D.: Preference-based search and machine learning for collaborative filtering: the ‘film-conseil’ movie recommender system. Inform. Interact. Intell. 1(1), 9–48 (2001)

    Google Scholar 

  81. Perugini, S., Goncalves, M.A., Fox, E.A.: Recommender systems research: a connection-centric survey. J. Intell. Inf. Syst. 23(2), 107–143, September (2004)

    Article  MATH  Google Scholar 

  82. Plantie, M., Montmain, J., Dray, G.: Movies recommenders systems: automation of the information and evaluation phases in a multi-criteria decision-making process. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) Proc. DEXA, LNCS vol. 3588, pp. 633–644. Springer, Berlin Heidelberg New York (2005)

  83. Price, B., Messinger, P.R.: Optimal recommendation sets: covering uncertainty over user preferences. Informs Ann. Meet., Denver 2004, AAAI (2005)

  84. Reich, E.: Users are individuals: individualizing user models. Int. J. Man-Mach. Stud. 18, 199–214 (1983)

    Article  Google Scholar 

  85. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering. In Proc. ACM CSCW, pp. 175–186. ACM, New York (1994)

  86. Resnick, P., Varian, H.R.: Recommender systems. Comm. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  87. Roh, T.H., Oh, K.J., Han, I.: The collaborative filtering recommendation based on SOM cluster-indexing CBR. Exp. Syst. Appl. 25, 413–423 (2003)

    Article  Google Scholar 

  88. Roy, B., Bouyssou, D.: Aide Multicritere a La Decision: Methodes et Cas. Economica, Paris (1993)

  89. Roy, B.: Multicriteria Methodology for Decision Aiding. Kluwer, Dordrecht (1996)

    MATH  Google Scholar 

  90. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of Recommendation Algorithms for E-Commerce. In Proc. of the ACM El. Comm. (EC’00), Minneapolis, Minnesota. Kluwer, The Netherlands (2000)

  91. Schaefer, R.: Rules for Using Multi-Attribute Utility Theory for Estimating a User’s Interests. In Proc. ABIS Worksh. ‘Adaptivität und Benutzermodellierung in interaktiven Softwaresystemen’, Dortmund, Germany, October (2001)

  92. Schafer, J.B.: DynamicLens: a dynamic user-interface for a meta-recommendation system. In Proc. “Beyond Personalization 2005” Work., Int. Conf. Intell. User Interf. (IUI 2005), San Diego, CA, January 9. Information Science (2005)

  93. Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Min. Knowl. Discov. 5, 115–153 (2001)

    Article  MATH  Google Scholar 

  94. Schein, A.I., Popescul, A., Ungar, L., Pennock, D.M.: CROC: a new evaluation criterion for recommender systems. Electr. Comm. Res. 5, 51–74 (2005)

    Article  MATH  Google Scholar 

  95. Schickel-Zuber, V., Faltings, B.: Hetereogeneous attribute utility model: a new approach for modelling user profiles for recommendation systems. In Proc. Worksh. Knowl. Discov. in the Web, Chicago, Illinois, USA, August (2005)

  96. Schmitt, C., Dengler, D., Bauer, M.: The MAUT-Machine: An Adaptive Recommender System. In Proc. ABIS Worksh. ‘Adaptivität und Benutzermodellierung in interaktiven Softwaresystemen,’ Hannover, Germany, October (2002)

  97. Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automatic ‘Word of Mouth.’ In Proc. Conf. Hum. Fact. Comput. Syst., Denver CO, USA. Addison-Wesley, New York (1995)

  98. Smyth, B., Cotter, P.: A personalized television listings service. Comm. ACM 43(8), 107–111, August (2000)

    Article  Google Scholar 

  99. Srikumar, K., Bhasker, B.: Personalized product selection in internet business. J. Electr. Comm. Res. 5(4), 216–227 (2004)

    Google Scholar 

  100. Stolze, M., Rjaibi, W.: Towards scalable scoring for preference-based item recommendation. IEEE Data Engin. Bullet. 24(3), 42–49 (2001)

    Google Scholar 

  101. Stolze, M., Stroebel, M.: Dealing with Learning in eCommerce Product Navigation and Decision Support: The Teaching Salesman Problem. In Proc. 2nd World Congr. Mass Custom. Person., Munich, Germany (2003)

  102. Terveen, L., Hill, W., Amento, B., McDonald, D., Creter, J.: PHOAKS: a system for sharing recommendations. Comm. ACM 40(3), 59–65, March (1997)

    Article  Google Scholar 

  103. Tewari, G., Youll, J., Maes, P.: Personalized location-based brokering using an agent-based intermediary architecture. Dec. Supp. Syst. 34, 127–137 (2002)

    Article  Google Scholar 

  104. Tso, K.H.L., Schmidt-Thieme, L.: Evaluation of attribute-aware recommender system algorithms on data with varying characteristics. In: Ng, W.K., Kitsuregawa, M., Li, J., Chang, K. (eds.) Advances in Knowledge Discovery and Data Mining. 10th Pacific-Asia Conf. (PAKDD’06), Singapore, April 9–12 (2006)

  105. Van Setten, M.: Supporting people in finding information: hybrid recommender systems and goal-based structuring. PhD thesis, Telematica Inst., Netherlands, Fund. Res. Ser. 016 (2005)

  106. Vincke, P.: Multicriteria Decision-Aid. J. Wiley, New York (1992)

    Google Scholar 

  107. Vuorikari, R., Manouselis, N., Duval, E.: Using metadata for storing, sharing, and reusing evaluations in social recommendation: the case of learning resources. In: Go, D.H., Foo, S. (eds.) Social Information Retrieval Systems: Emerging Technologies and Applications for Searching the Web Effectively. Idea Group Publishing, Hershey, PA, in press.

  108. Wang, F.-H., Shao, H.-M.: Effective personalized recommendation based on time-framed navigation clustering and association mining. Exp. Syst. Appl. 27, 365–377 (2004)

    Article  Google Scholar 

  109. Wang, P.: Recommendation based on personal preference. In: Zhang, Y., Kandel, A., Lin, T., Yao, Y. (eds.) Comput. Web Intell.: Intell. Techn. Web Appl., World Scientific (2004)

  110. Wang, Y.-F., Chuang, Y.-L., Hsu, M.-H., Keh, H.-C.: A personalized recommender system for the cosmetic business. Exp. Syst. Appl. 26, 427–434 (2004)

    Article  Google Scholar 

  111. Wei, C.-P., Shaw, M.J., Easley, R.F.: A survey of recommendation systems in electronic commerce. In: Rust, R.T., Kannan, P.K. (eds.) E-Serv.: New Dir. in Theor. and Pract., M. E. Sharpe (2002)

  112. Yano, E., Sueyoshi, E., Shinohava, I., Kato, T.: Development of a recommendation system with multiple subjective evaluation process models. In Proc. 2nd Int. Conf. Cyberworlds, Singapore, December. IEEE Computer Society, Washington, DC (2003)

  113. Yu, C.-C.: Designing a web-based consumer decision support systems for tourism services. In Proc. 4th Int. Conf. Electr. Comm., Hong Kong, October (2002)

  114. Yu, C.-C.: Innovation, management & strategy: a web-based consumer-oriented intelligent decision support system for personalized e-services. In Proc. 6th Int. Conf. Electr. Comm., March. ACM, New York (2004)

  115. Yuan, S.-T., Cheng, C.: Ontology-based personalized couple clustering for heterogeneous product recommendation in mobile marketing. Exp. Syst. Appl. 26, 461–476 (2004)

    Article  Google Scholar 

  116. Yuan, S.-T., Tsao, Y.W.: A recommendation mechanism for contextualized mobile advertising. Exp. Syst. Appl. 24, 399–414 (2003)

    Article  Google Scholar 

  117. Zeleny, M.: Linear Multiobjective Programming. Springer, Berlin Heidelberg New York (1974)

    MATH  Google Scholar 

  118. Zimmerman, J., Kurapati, K., Buczak, A.L., Schaffer, D., Martino, J., Gutta, S.: TV personalization system: design of a TV show recommender engine and interface. In: Ardissono, L., Kobsa, A., Maybury, M. (eds.) Personalized Digital Television: Targeting Programs to Individual Viewers, vol. 6. Hum.-Comp. Inter. Ser., Kluwer, Dordrecht (2004)

  119. Zimmerman, J., Parameswaran, L., Kurapati, K.: Celebrity recommender. In Proc. AH’2002 Work. “Recommendation and Personalization in eCommerce”, Málaga, Spain, May 28th (2002)

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  1. Informatics Laboratory, Division of Informatics, Mathematics & Statistics, Department of Science, Agricultural University of Athens, 75 Iera Odos str., 118 55, Athens, Greece

    Nikos Manouselis & Constantina Costopoulou

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Manouselis, N., Costopoulou, C. Analysis and Classification of Multi-Criteria Recommender Systems. World Wide Web 10, 415–441 (2007). https://doi.org/10.1007/s11280-007-0019-8

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