TY - JOUR
AU1 - Lawrence, R.D.
AU2 - Almasi, G.S.
AU3 - Kotlyar, V.
AU4 - Viveros, M.S.
AU5 - Duri, S.S.
AB - We describe a personalized recommender system designed to suggest new products to supermarket shoppers. The recommender functions in a pervasive computing environment, namely, a remote shopping system in which supermarket customers use Personal Digital Assistants (PDAs) to compose and transmit their orders to the store, which assembles them for subsequent pickup. The recommender is meant to provide an alternative source of new ideas for customers who now visit the store less frequently. Recommendations are generated by matching products to customers based on the expected appeal of the product and the previous spending of the customer. Associations mining in the product domain is used to determine relationships among product classes for use in characterizing the appeal of individual products. Clustering in the customer domain is used to identify groups of shoppers with similar spending histories. Cluster-specific lists of popular products are then used as input to the matching process.
TI - Personalization of Supermarket Product Recommendations
JF - Data Mining and Knowledge Discovery
DO - 10.1023/A:1009835726774
DA - 2004-10-09
UR - https://www.deepdyve.com/lp/springer-journals/personalization-of-supermarket-product-recommendations-kvzPIBDjn5
SP - 11
EP - 32
VL - 5
IS - 2
DP - DeepDyve
ER -