ABSTRACT
Recommender systems estimate the conditional probability P(χj|χi) of item χj being bought, given that a customer has already purchased item χi. While there are different ways of approximating this conditional probability, the expression is generally taken to refer to the frequency of co-occurrence of items in the same basket, or other user-specific item lists, rather than being seen as the co-occurrence of χj with χi as a proportion of all other items bought alongside χi. This paper proposes a probabilistic calculus for the calculation of conditionals based on item rather than basket counts. The proposed method has the consequence that items bough together as part of small baskets are more predictive of each other than if they co-occur in large baskets. Empirical results suggests that this may result in better take-up of personalised recommendations.
- J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998. Google ScholarDigital Library
- T. Brijs, G. Swinnen, K. Vanhoof, and G. Wets. Using association rules for product assortment decisions: A case study. In Knowledge Discovery and Data Mining, pages 254--260, 1999. Google ScholarDigital Library
- M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Transactions of Information System, 22(1):143--177, 2004. Google ScholarDigital Library
- A. McCallum and K. Nigam. A comparison of event models for naive bayes text classification. In In AAAI-98 Workshop on Learning for Text Categorization, 1998.Google Scholar
- V. Robles, n. P. Larra E. Menasalvas, M. S. Pérez, and V. Herves. Improvement of naive bayes collaborative filtering using interval estimation. In WI '03: Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence, 2003. Google ScholarDigital Library
- C. M. Sordo-Garcia, M. B. Dias, M. Li, W. El-Deredy, and P. J. G. Lisboa. Evaluating retail recommender systems via retrospective data: Lessons learnt from a live-intervention study. In The 2007 International Conference on Data Mining, DMIN'07, 2007.Google Scholar
- T. Zhang and V. S. Iyengar. Recommender systems using linear classifiers. Journal of Machine Learning Research, 2:313--334, 2002. Google ScholarDigital Library
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