Informed Recommender: Basing Recommendations on Consumer Product Reviews

SILVANA ACIAR; DEBBIE ZHANG; SIMEON SIMOFF; JOHN DEBENHAM

IEEE INTELLIGENT SYSTEMS

IEEE Computer Society

Año: 2007 vol. 22 p. 39 - 47

1541-1672

Recommender systems attempt to predict items in which a user might be interested, given some information about the user?s and items? profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques. We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed Recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology.