Abstract:
Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among users' preferences. This paper presents an approach to using data mining for e-commerce. It applies association rule mining to collaborative recommender systems, which recommend articles to a user on the basis of other users’ ratings for these articles as well as the similarities between this user’s and other users’ tastes. This approach makes recommendations by exploring associations between users, associations between articles, and a combination of the two. It is found that association rules are quite appropriate for collaborative recommendation domains and that better performance can be achieved that is comparable to current state of the art in recommender systems research.
