Improving E-commerce recommender systems by the identification of seasonal products

AAAI Workshop - Technical Report

Volumn WS-07-08, Issue , 2007, Pages 92-99

  Stormer, Henrik ^a  

^a UNIVERSITY OF FRIBOURG   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

E-COMMERCE; ELECTRONIC COMMERCE APPLICATIONS; INTELLIGENT TECHNIQUES; MARKETING CAMPAIGNS; ONLINE SHOPPING; REAL-LIFE DATA; RECOMMENDER SYSTEMS; SEASONAL PRODUCTS; TECHNICAL REPORTS; WEB PERSONALIZATION;

COMMERCE; ELECTRONIC COMMERCE; INFORMATION RETRIEVAL SYSTEMS; INTELLIGENT SYSTEMS; MARKETING;

ONLINE SYSTEMS;

EID: 51849159509     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Adomavicius, G., and Tuzhilin, A. 2005. Towards the next generation of recommender systems: A survey of the state-of-the-art and possible extentions. IEEE Transactions on Knowledge and Data Engineering 17(6):734-749.
2. Basu, C.; Hirsh, H.; and Cohen, W. 1998. Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the 1998 workshop on recommender systems, 11-15.
3. Breese, J. S.; Heckerman, D.; and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence.
4. Burke, R. 2002. Hybrid recommender systems, survey and experiments. User Modeling and User Adapted Interaction 12(4):331-370.
5. Chao, D. L.; Balthrop, J.; and Forrest, S. 2005. Adaptive, radio: Achieving consensus using negative preferences. In Proceedings of the International ACM SIGGROUP Conference on Supporting Group Work, 120-123.
6. Deshpande, M., and Karypis, G. 2004. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems 22(1): 143-177.
7. Herlocker, J. L.; Konstan, J. A.; Terveen, L. G.; and Riedl, J. T. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22(1):5-53.
8. Hofmann, T. 2004. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems 22(1):89-115.
9. Kawasaki, Y. 2006. A structural time series model facilitating flexible seasonality. In Proceedings of the Conference on Seasonality, Seasonal Adjustment and Their Implications for Short-term Analysis and Forecasting.
10. Linden, G.; Smith, B.; and York, J. 2003. Amazon.com recommendations. IEEE Internet Computing 3:76-80.
11. Ling, K.; Beenen, G.; Ludford, P.; Wang, X.; Chang, K.; ; Li, X.; Frankowski, D.; Terveen, L.; Rashid, A. M.; Resnick, P.; and Kraut, R. E. 2005. Using social psychology to motivate contributions to online communities. Journal of Computer-Mediated Communication 10(4).
12. McCarthy, J. F., and Anagnost, T. D. 1998. Musicfx: An arbiter of group preferences for computer supported collaborative workouts. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, 363-372.
13. McMath, R. 1994. Product proliferation. Mediaweek 4:34-35.
14. Radas, S., and Shugan, S. M. 1998. Seasonal marketing and timing new product introductions. Journal of Marketing Research 35(3):296-315.
15. Resnick, P.; Iacovou, N.; Suchack, M.; Bergstrom, P.; and Riedl, J. 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work.
16. Sarwar, B.; Karypis, G.; Konstan, J.; and Riedl, J. 2000. Analysis of recommendation algorithms for ecommerce. In Proceedings of the ACM Conference for Electronic Commerce.
17. Schafer, J. B.; Konstan, J.; and Riedl, J. 1999. Recommender systems in e-commerce. In Proceedings of the ACM Conference on Electronic Commerce.
18. Schafer, J. B.; Konstan, J. A.; and Riedl, J. 2001. E-commerce recommendation applications. Data Mining and Knowledge Discovery 5(1-2): 115-153.
19. Zellner, A., ed. 1979. NBER-Census Conference on Seasonal Economic Time Series. Government Printing Office.