Book Recommender System using Fuzzy Linguistic Quantifier and Opinion Mining

The recommender systems are being used immensely to promote various services, products and facilities of daily life. Due to the success of this technology, the reliance of people on the recommendations of others is increasing with tremendous pace. One of the best and easiest ways to acquire the suggestions of the other like-minded and neighbor customers is to mine their opinions about the products and services. In this paper, we present a feature based opinion extraction and analysis from customers’ online reviews for books. Ordered Weighted Aggregation (OWA), a well-known fuzzy averaging operator, is used to quantify the scores of the features. The linguistic quantifiers are applied over extracted features to ensure that the recommended books have the maximum coverage of these features. The results of the three linguistic quantifiers, ‘at least half’, ‘most’ and ‘as many as possible’ are compared based on the evaluation metric - precision@5. It is evident from the results that quantifier ‘as many as possible’ outperformed others in the aforementioned performance metric. The proposed approach will surely open a new chapter in designing the recommender systems to address the expectation of the users and their need of finding relevant books in a better way.

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