Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Recommender systems using linear classifiers
The Journal of Machine Learning Research
Improvement of Naïve Bayes Collaborative Filtering Using Interval Estimation
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Mining recommendations from the web
Proceedings of the 2008 ACM conference on Recommender systems
The value of personalised recommender systems to e-business: a case study
Proceedings of the 2008 ACM conference on Recommender systems
Grocery shopping recommendations based on basket-sensitive random walk
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
ROAuth: recommendation based open authorization
Proceedings of the Seventh Symposium on Usable Privacy and Security
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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.