Institute of Information Theories and Applications FOI ITHEA
Abstract:
Recommender systems are now widely used in e-commerce applications to assist customers to find
relevant products from the many that are frequently available. Collaborative filtering (CF) is a key component of
many of these systems, in which recommendations are made to users based on the opinions of similar users in a
system. This paper presents a model-based approach to CF by using supervised ARTMAP neural networks (NN).
This approach deploys formation of reference vectors, which makes a CF recommendation system able to
classify user profile patterns into classes of similar profiles. Empirical results reported show that the proposed
approach performs better than similar CF systems based on unsupervised ART2 NN or neighbourhood-based
algorithm.
