Title:	IMPROVING DIVERSITY AND RELEVANCY OF ECOMMERCE RECOMMENDER SYSTEMS THROUGH NLP TECHNIQUES
Author(s):	Andriy Shepitsen , Noriko Tomuro
ISBN:	978-972-8924-89-8
Editors:	Sandeep Krishnamurthy
Year:	2009
Edition:	Single
Keywords:	Recommender Systems, E-Business Application, Reviews, NLP, Knowledge Discovery
Type:	Full Paper
First Page:	147
Last Page:	154
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	Emerging Web 2.0 technologies offer abundance of opportunities for e-commerce systems to improve the effectiveness of recommendation. For example, many e-commerce sites allow users to enter reviews together with ratings in order to obtain more feedback on their products and services. In this paper we present an approach which considers user reviews in generating personalized recommendations in e-commerce recommender systems. Our approach is novel in that the system incorporates user reviews as an additional dimension in representing the inter-relations between items and user preferences. By utilizing user reviews as the fourth dimension in addition to the traditional three dimensions of items, users and ratings, our system can generate recommendations which are more relevant to users’ interests. To analyze user reviews we utilize techniques from Natural Language Processing (NLP), a sub-field in Artificial Intelligence (AI). We extract terms/words from user reviews and analyze their parts-of-speech (POS). Then we use nouns and adjectives (only) to represent a user review, and develop a new recommendation model, which we call RecRank, that utilizes all four dimensions. We also incorporate the notion of authority – items which are frequently mentioned in other items’ reviews are considered popular and authoritative, thus should be ranked higher in the recommendations. We run several experiments on a real e-commerce data (Amazon books) and compare results with other standard recommendation approaches, namely item- and collaborative-based filtering and association rule mining algorithm. The results showed that user reviews were effective in increasing the diversity as well as relevancy of the recommended items.
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