Fuzzy-genetic approach to recommender systems based on a novel hybrid user model

Expert Systems with Applications

Volumn 35, Issue 3, 2008, Pages 1386-1399

  Al Shamri, Mohammad Yahya H ^a   Bharadwaj, Kamal K ^a  

^a JAWAHARLAL NEHRU UNIVERSITY   (India)

Author keywords

Collaborative filtering; Fuzzy sets; Recommender systems; User model; Web personalization

Indexed keywords

COMPUTER GRAPHICS; COMPUTER NETWORKS; FILTRATION; INFORMATION RETRIEVAL SYSTEMS; SCALABILITY;

CLASSICAL APPROACHES; COLLABORATIVE FILTERING (CF); COMPUTATIONAL RESULTS; ELSEVIER (CO); FILTERING TECHNIQUES; GENETIC APPROACH; HYBRID FUZZY; MODEL BASED (OPC); RECOMMENDER SYSTEMS; SIGNIFICANT REDUCTION; SYSTEM COMPLEXITY; USER MODELLING;

FUZZY LOGIC;

EID: 44949123611     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.08.016     Document Type: Article

References (29)

1. Adomavicius G., and Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transaction on Knowledge and Data Engineering 17 6 (2005) 734-749
2. Balabanovic M., and Shoham Y. Fab: Content-based, collaborative recommendation. Communications of the ACM 40 3 (1997) 66-72
3. Basu, C., Hirsh, H., & Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the 15th national conference on artificial intelligence, Madison, Wisconsin.
4. Breese J.S., Heckerman D., and Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th conference on uncertainty in artificial intelligence (1998), Morgan Kaufmann, Madison, WI/San Francisco, CA 43-52
5. Burke R. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12 (2002) 331-370
6. Dumitrescu D., Lazzerini B., and Jain L.C. Fuzzy sets and their application to clustering and training (2000), CRC Press LLC
7. Eirinaki M., and Vazirgiannis M. Web mining for web personalization. ACM Transactions on Internet Technology 3 1 (2003) 1-27
8. Froschl, C. (2005). User Modeling and User Profiling in Adaptive E-learning Systems. Master Thesis, Graz University of Technology, Graz, Austria.
9. Gadi, T., Daoudi, R. B. M., & Matusiak, S. (1999). Fuzzy similarity measure for shape retrieval. In Vision Interface '99, Trois-Rivières, Canada (pp. 19-21).
10. Goldberg D. Genetic algorithms in search, optimization, and machine learning (1989), Pearson Education
11. Goldberg D., Nichols D., Oki B.M., and Terry D. Using collaborative filtering to weave an information tapestry. Communications of the ACM 35 12 (1992) 61-70
12. Han J., and Kamber M. Data mining, concepts and techniques (2001), Academic Press
13. Klir G., and Yuan B. Fuzzy sets and fuzzy logic, theory and applications (1995), Prentice-Hall
14. Koch, N. (2000). Software Engineering for Adaptive Hypermedia Systems. PhD thesis, Ludwig-Maximilians-University Munich/Germany.
15. Krulwich B. Lifestyle Finder: Intelligent user profiling using large-scale demographic data. Artificial Intelligence Magazine 18 2 (1997) 37-45
16. Lang, K. (1995). NewsWeeder: Learning to filter netnews. In Proceedings of the 12th international conference on machine learning, Tahoe City, CA.
17. Linden G., Smith B., and York J. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7 1 (2003) 76-80
18. Massa P., and Avesani P. Trust-aware collaborative filtering for recommender systems. CoopIS/DOA/ODBASE (1) (2004) 492-508
19. Michalski R. Learnable evolution model: Evolutionary processes guided by machine learning. Machine Learning 38 (2000) 9-40
20. Miller, B. N., Albert, I., Lam, S. K., Konstan, J. A., & Riedl, J. (2003). MovieLens unplugged: Experiences with an occasionally connected recommender system. In Proceedings of the international conference on intelligent user interfaces.
21. Nasraoui, O., & Petenes, C. (2003). An intelligent web recommendation engine based on fuzzy approximate reasoning. In Proceedings of the IEEE international conference on fuzzy systems (pp. 1116-1121).
22. Pazzani M. A framework for collaborative, content-based, and demographic filtering. Artificial Intelligence Review (1999) 393-408
23. Resnick P., Iakovou N., Sushak M., Bergstrom P., and Riedl J. GroupLens: An open architecture for collaborative filtering of Netnews. Proceedings of ACM conference on computer supported cooperative work (1994), Chapel Hill, NC 175-186
24. Shahabi, C., Banaei-Kashani, F., Chen, Y., & McLeod, D. (2001). Yoda: An accurate and scalable web-based recommendation systems. In Proceedings of the sixth international conference on cooperative information systems (CoopIS 2001), Trento, Italy.
25. Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating 'Word of Mouth'. In Proceedings of the conference on human factors in computing systems (CHI'95), Denver (pp. 210-217).
26. Suryavanshi, B. S., Shiri, N., & Mudur, S. P. (2005). A fuzzy hybrid collaborative filtering technique for web personalization. In Proceedings of the third workshop on intelligent techniques for web personalization (ITWP'05), Edinburgh, Scotland, UK.
27. Ujjin S., and Bentley P. Using evolutionary to learn user preferences. In: Tan K., Lim M., Yao X., and Wang L. (Eds). Recent advances in simulated evolution and learning (2004), World Scientific Publishing 20-40
28. Vozalis, E., & Margaritis, K. G. (2003). Analysis of recommender systems' algorithms. In Proceedings of the sixth Hellenic-European conference on computer mathematics and its applications (HERCMA). Athens, Greece.
29. Zadeh L.A. Fuzzy sets. Information Control 8 (1965) 338-353