Fuzzy logic methods in recommender systems

ABSTRACT

Here we consider methodologies for constructing recommender systems. The approaches studied here differ from collaborative filtering, they are based solely on the preferences of the single individual for whom we are providing the recommendation and make no use of the preferences of other collaborators. We have called these reclusive methods. Another important feature distinguishing these reclusive methods from collaborative methods is that they require a representation of the objects. Considerable use is made of fuzzy set methods for the representation and subsequent construction of justifications and recommendation rules. It is pointed out these reclusive methods rather than being competitive with collaborative methods are complementary.

REFERENCES

• 1: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 2: James C. Bezdek , Mikhil R. Pal , James Keller , Raghu Krisnapuram, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing, Kluwer Academic Publishers, Norwell, MA, 1999
• 3: Didier Dubois , Henri Prade , Florence Sèdes, Fuzzy Logic Techniques in Multimedia Database Querying: A Preliminary Investigation of the Potentials, IEEE Transactions on Knowledge and Data Engineering, v.13 n.3, p.383-392, May 2001 [doi>10.1109/69.929896]
• 4: David Goldberg , David Nichols , Brian M. Oki , Douglas Terry, Using collaborative filtering to weave an information tapestry, Communications of the ACM, v.35 n.12, p.61-70, Dec. 1992 [doi>10.1145/138859.138867]
• 5: {5} H. Kautz, Recommender Systems, AAAI Press, Menlo Park, CA, 1998.
• 6: Joseph A. Konstan , Bradley N. Miller , David Maltz , Jonathan L. Herlocker , Lee R. Gordon , John Riedl, GroupLens: applying collaborative filtering to Usenet news, Communications of the ACM, v.40 n.3, p.77-87, March 1997 [doi>10.1145/245108.245126]
• 7: {7} P. Perny, J.-D. Zucker, Collaborative filtering methods based on fuzzy preference relations, EUROFUSE-SIC'99, Budapest, 1999.
• 8: Paul Resnick , Hal R. Varian, Recommender systems, Communications of the ACM, v.40 n.3, p.56-58, March 1997 [doi>10.1145/245108.245121]
• 9: J. Ben Schafer , Joseph A. Konstan , John Riedl, E-Commerce Recommendation Applications, Data Mining and Knowledge Discovery, v.5 n.1-2, p.115-153, January-April 2001 [doi>10.1023/A:1009804230409]
• 10: Upendra Shardanand , Pattie Maes, Social information filtering: algorithms for automating “word of mouth”, Proceedings of the SIGCHI conference on Human factors in computing systems, p.210-217, May 07-11, 1995, Denver, Colorado, United States [doi>10.1145/223904.223931]
• 11: {11} M. Sugeno, Fuzzy measures and fuzzy integrals: a survey, in: M.M. Gupta, G.N. Saridis, B.R. Gaines (Eds.), Fuzzy Automata and Decision Process, North-Holland, Amsterdam, 1977, pp. 89-102.
• 12: Toshiro Terano , Kiyoji Asai , Michio Sugeno , Charles Aschmann, Applied fuzzy systems, Academic Press Professional, Inc., San Diego, CA, 1994
• 13: Ronald R. Yager, On ordered weighted averaging aggregation operators in multicriteria decisionmaking, IEEE Transactions on Systems, Man and Cybernetics, v.18 n.1, p.183-190, January/February 1988 [doi>10.1109/21.87068]
• 14: Ronald R. Yager, Families of OWA operators, Fuzzy Sets and Systems, v.57 n.3, p.125-148, Aug. 10, 1993 [doi>10.1016/0165-0114(93)90194-M]
• 15: {15} R.R. Yager, Quantifier guided aggregation using OWA operators, Internat. J. Intell. Systems 11 (1996) 49-73.
• 16: Ronald R. Yager, Targeted E-commerce Marketing Using Fuzzy Intelligent Agents, IEEE Intelligent Systems, v.15 n.6, p.42-45, November 2000 [doi>10.1109/5254.895859]
• 17: {17} R.R. Yager, Veristic variables, IEEE Trans. Systems Man Cybernet. Part B: Cybernetics 30 (2000) 71-84.
• 18: Ronald R. Yager, A Hierarchical Document Retrieval Language, Information Retrieval, v.3 n.4, p.357-377, December 2000 [doi>10.1023/A:1009911900286]
• 19: {19} R.R. Yager, D.P. Filev, Approximate clustering via the mountain method, IEEE Trans. Systems Man Cybernet. 24 (1994) 1279-1284.
• 20: {20} R.R. Yager, D.P. Filev, Generation of fuzzy rules by mountain clustering, J. Intell. Fuzzy Systems 2 (1994) 209-219.
• 21: Ronald R. Yager , Janusz Kacprzyk, The ordered weighted averaging operators: theory and applications, Kluwer Academic Publishers, Norwell, MA, 1997
• 22: {22} L.A. Zadeh, A computational approach to fuzzy quantifiers in natural languages, Comput. Math. Appl. 9 (1983) 149-184.
• 23: Lotfi A. Zadeh, Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems, v.90 n.2, p.111-127, Sept. 1, 1997 [doi>10.1016/S0165-0114(97)00077-8]
• 24: {24} L.A. Zadeh, A new direction in AI--toward a computational theory of perceptions, Artif. Intell. Mag. 22 (1) (2001) 73-84.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Uncertainty, "fuzzy," and probabilistic reasoning

Additional Classification:
  F. Theory of Computation
  F.3 LOGICS AND MEANINGS OF PROGRAMS
      F.3.1 Specifying and Verifying and Reasoning about Programs
          Subjects: Logics of programs

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
  I.5 PATTERN RECOGNITION
      I.5.1 Models
          Subjects: Fuzzy set

General Terms:
Algorithms, Theory

Keywords:
collaborative filtering, customization, fuzzy methods, recommender systems