A multi-stage collaborative filtering approach for mobile recommendation

Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication, ICUIMC'09

Volumn , Issue , 2009, Pages 88-97

  Li, Li Hua ^a   Lee, Fu Ming ^a   Chen, Yu Chun ^b   Cheng, Chieh Yu ^a  

^a CHAOYANG UNIVERSITY OF TECHNOLOGY   (Taiwan)

^b SourceCode Technology Holdings Inc   (Taiwan)

Author keywords

Adaptive Resonance Theory (ART); Collaborative Filtering (CF); Event recommendation; Recommendation systems (RS)

Indexed keywords

ADAPTIVE RESONANCE THEORY (ART); ADAPTIVE RESONANCE THEORY NETWORKS; COLD START PROBLEMS; COLLABORATIVE FILTERING; COLLABORATIVE FILTERING (CF); EVENT RECOMMENDATION; KEY FACTORS; LOCATION BASED; MOBILE USERS; MULTI-STAGE; PERSONAL PROFILE; PERSONALIZED SERVICE; RECOMMENDATION SYSTEMS; RECOMMENDATION SYSTEMS (RS); SEQUENTIAL-PATTERN MINING; SERVICE PROVIDER; USER SATISFACTION;

ARTS COMPUTING; GLOBAL SYSTEM FOR MOBILE COMMUNICATIONS; INFORMATION MANAGEMENT; PROFITABILITY; RAPID SOLIDIFICATION; RESONANCE; SIGNAL FILTERING AND PREDICTION; STARTING;

WIRELESS NETWORKS;

EID: 70349152192     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1516241.1516258     Document Type: Conference Paper

References (30)

1. Agrawal, R. and Srikant, R., "Fast Algorithms for Mining Association Rules," Proceedings of the 20th VLDB Conference on Data Engineering, pp. 3-14, 1994.
2. Agrawal, R. and Srikant, R., "Mining Sequential Patterns," Proceedings of the 11th Int'l Conf., pp. 487-499, 1995.
3. Amberg, M., Figge, S., and Wehrmann, J., A Cooperation Model for Personalised and Situation Dependent Services in Mobile Networks, Advanced Conceptual Modeling Techniques, LNCS 2784, Springer-Verlag Berlin Heidelberg, pp. 136-146, 2003.
4. Carpenter, G. A. and Grossberg, S., Adaptive Resonance Theory, The Handbook of Brain Theory and Neural Networks, In M.A. Arbib (ed), Second Edition, Cambridge, MA: MIT Press, 2003, pp. 87-90, 2003.
5. Cho, Y. H. and Kim, J. K., "Application of Web Usage Mining and Product Taxonomy to Collaborative Recommendations in E-Commerce," Expert Systems with Applications, Vol. 26, No. 2, pp. 233-246, 2004.
6. Deshpande, M. and Karypis, G., "Item-Based Top-iV Recommendation Algorithms," ACM Transactions on Information Systems, Vol. 22, No. 1, pp. 143-177, 2004.
7. Figge, S., "Situation-Dependent Services - A Challenge for Mobile Network Operators," Journal of Business Research, Vol. 57, No. 12, pp. 1416-1422, 2004.
8. Gerstheimer, O. and Lupp, C., "Needs Versus Technology-The Challenge to Design Third-Generation Mobile Applications," Journal of Business Research, Vol. 57, No. 12, pp. 1409-1415, 2004.
9. Grossberg, S., "Adaptive Pattern Classification and Universal Recoding, II: Feedback, Expectation, Olfaction, and Illusions," Biological Cybernetics, Vol. 23, No. 4, pp. 187-202, 1976.
10. Ho, S. Y. and Kwok, S. PL, "The Attraction of Personalized Service for Users in Mobile Commerce: An Empirical Study," ACM SIGecom Exchanges, Vol. 3, No. 4, pp. 10-18, 2003.
11. Kaasinen, E., "User Needs for Location-aware Mobile Services," Personal and Ubiquitous Computing, Vol. 7, No. 1, pp. 70-79, 2003.
12. Kim, J. K., Cho, Y. H., Kim, W. J., Kim, J. R., and Suh, J. PL, "A Personalized Recommendation Procedure for Internet Shopping Support," Electronic Commerce Research and Applications, Vol. 1, No. 3-4, pp. 301-313, 2002.
13. Kuo, R. J., Liao, J. L., and Tu, C, "Integration of ART2 Neural Network and Genetic K-means Algorithm for Analyzing Web Browsing Paths in Electronic Commerce," Decision Support Systems, Vol. 40, No. 2, pp. 355-374, 2005.
14. Lee, C. H., Kim, Y. H., and Rhee, P.K., "Web Personalization Expert with Combining Collaborative Filtering and Association Rule Mining Technique," Expert Systems with Applications, Vol. 21, No. 3, pp. 131-137, 2001.
15. Li, Y., Lu, L., and Xuefeng, L., "A Hybrid Collaborative Filtering for Multiple-Interests and Multiple-Content Recommendation in E-Commerce," Expert Systems with Applications, Vol. 28, No. 1, pp. 67-77, 2005.
16. Melville, P., Mooney, R. J., and Nagarajan, R., "Content-Boosted Collaborative Filtering," Pro. of The SIGIR-2001 Workshop on Recommender Sys., Sep. 2001, pp. 1-9, 2001.
17. Patterson, D. W., Artificial Neural Networks: Theory and Applications, Prentice Hall, 1st edition, 1996.
18. Pinheiro, M. K., Marlene, V.O., Jerome, G., and Herve, M., "Context-aware Filtering for Collaborative Web Systems: Adapting The Awareness Information to The User's Context," Proceedings of The 2005 ACM Symposium on Applied Computing, pp. 1668-1673, 2005.
19. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., "Analysis of Recommendation Algorithms for E-Commerce," Proceedings of The 2nd ACM Conference on Electronic Commerce, pp. 158-167, 2000.
20. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., "Item-Based Collaborative Filtering Recommendation Algorithms," Proceedings of The WWW 10 Conference, Hong Kong, pp. 285-295, 2000.
21. Scharl, A., Dickinger, A., and Murphy, J., "Diffusion and Success Factors of Mobile Marketing," Electronic Commerce Research and Applications, Vol. 4, No. 2, pp. 159-173, 2005.
22. Schafer, J. B., Konstan, J., and Riedl, J., "Recommender Systems in E-Commerce," Proceedings of the First ACM Conference on Electronic Commerce, Denver, pp. 158-166, 1999.
23. Tan, P. N., Steinbach, M., and Kumar, V., Introduction to Data Mining, Person International Edition, 2006.
24. Turban, E., King, D., Lee, J., and Viehland, D., Electronic Commerce: A Managerial Perspective, Prentice Hall International, 3rd edition, 2004.
25. Wang, F. H. and Shao, H. M., "Effective Personalized Recommendation Based on Time-framed Navigation Clustering and Association Mining," Expert Systems with Applications, Vol. 27, No. 3, pp. 365-377, 2004.
26. Weng, S. S. and Liu, M. J., "Feature-based Recommendation for One-to-One Marketing," Expert Systems with Applications, Vol. 26, No. 4, pp. 493-508, 2004.
27. Yu, K., Schwaighofer, A., Tresp, V., Xu, X., and Kriegel, H. P., "Probabilistic Memory-Based Collaborative Filtering," IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 1, pp. 56-69, 2004.
28. Zeng, C, Xing, C. X., and Xhou, L. Z., "Similarity Measure and Instance Selection for Collaborative Filtering," Proceedings of WWW 2003, Budapest, Hungary, pp. 652-658, 2003.
29. Amazon, http://www.amazon.com, 2005.
30. eBay, http://www.eBay.com, 2005.