Recommender System Based on Consumer Product Reviews

Universitat de Girona, Girona, Catalonia, Spain
12/2006; DOI: 10.1109/WI.2006.144
Source: OAI


Consumer reviews, opinions and shared experiences in the use of a product is a powerful source of information about consumer preferences that can be used in recommender systems. Despite the importance and value of such information, there is no comprehensive mechanism that formalizes the opinions selection and retrieval process and the utilization of retrieved opinions due to the difficulty of extracting information from text data. In this paper, a new recommender system that is built on consumer product reviews is proposed. A prioritizing mechanism is developed for the system. The proposed approach is illustrated using the case study of a recommender system for digital cameras

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    • ", and there is literature on how such explicit user feedback can be applied to recommender systems [Garcia Esparza et al. 2012; Aciar et al. 2006]. Furthermore , Desrosiers and Karypis [2010] proposed an alternative algorithm for computing similarities—a key function of collaborative-based recommender systems—using nonnumerical ratings. "

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    • "Moreover, they also comment on the advantages of using user-generated content for recommender systems; such as, for example, providing a better rationale for recommended products and increasing user trust in the system. One of the first attempts to build a recommender system based on user-generated review data is described in [1]. Here, an ontology is used to extract concepts from camera reviews and recommendations are provided based on users' requests about a product; for example, " I would like to know if Sony361 is a good camera, specifically its interface and battery consumption " . "
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