Informed Recommender: Basing Recommendations on Consumer Product Reviews

Authors:
Silvana Aciar;Debbie Zhang;Simeon Simoff;John Debenham
Affiliations:
University of Girona, Spain;University of Technology, Sydney;University of Technology, Sydney;University of Technology, Sydney
Venue:
IEEE Intelligent Systems
Year:
2007

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Abstract

Consumer reviews, opinions, and shared experiences in using a product are a powerful source of information that recommender systems can use. Despite the importance and value of such information, no comprehensive mechanism formalizes the opinions' selection, retrieval, and use owing to the difficulty of extracting information from text data. A new recommender system prioritizes consumer product reviews on the basis of the reviewer's level of expertise in using a product. The system uses text mining techniques to map each piece of each review comment into an ontology. Using consumer reviews also helps solve the cold-start problem that plagues traditional approaches. This article is part of a special issue on Recommender Systems.