A New Approach For Item Choice Recommendations
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Market basket databases contain historical data on prior customer choices where each customer has selected a subset of items, a market, out of a large but finite set. This data can be used to generate a dynamic recommendation of new items to a customer who is in the process of making the item choices. We develop a statistical model to compute the probability of a new item becoming the next choice given the current partial basket, and make a recommendation list based on a ranking of this probability. What makes our approack different from the usual collaborative filtering approaches, is that we account not only for the choice making, or buying, associated with the items present in the partial basket (associative buying), but also for the fact that a customer exercises an independent choice unrelated to the existing partial basket (renewal buying). We compute the probability of both renewal choice and associative choice given the partial basket content, and obtain the probabilities for each item given one of these two buying modes. Our experiments on the publicly available “ms.com” data set shows our new approach yields faster and more accurate prediction compared to other techniques that have been proposed for this problem in the literature.
KeywordsCollaborative Filter Computer Support Cooperative Work Market Basket Recommendation List Current Basket
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- 2.Breece J., Heckerman D. And Kadie C., “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proc. of Fourteenth Conf. on Uncertainty in Artificial Intelligence, Morgan Kaufman, Madison Wisc., 1998.Google Scholar
- 3.Heckerman D., Chickering D.M., Meek C., Rounthwaite R. And Kadie C, “Dependency Network for Collaborative Filtering and Visualization”, Proc. of Sixteenth Conf. on Uncertainty in Artificial Intelligence, Stanford, CA, pp. 264–273, 2000Google Scholar
- 4.Borges J. and Levene M., “Data Mining of User Navigation Patterns,” Web Usage Analysis and User Profiling”, WEBKDD-99 Workshop revised papers, Lecture Notes in Artificial Intelligence, Vol. 1836, pp. 92–110, 2000.Google Scholar
- 6.Iyengar V.S., Zhang T., “Emprical Study of Recommender Systems using Linear Classifiers”, to appear in Proc. of PAKDD-01, April 2001.Google Scholar