Book Recommender System Using Fuzzy Linguistic Quantifiers

The recommender systems are used to facilitate the users with appropriate choices according to their preferences for various online services. Due to the increasing need, various recommendation systems have been developed including recommendation for music, book, movie, etc. The book recommendation technique usually explores the rating of the users for the particular product to recommend it to other users. Instead of utilizing users’ reviews, we have proposed an authorities recommendation approach which exploits ranking of the books by different top-ranked universities. These rankings are aggregated using OWA. Ordered Weighted Aggregation (OWA), a well-known fuzzy averaging operator, is used to aggregate different rankings of the books given by respective universities. The rank of the books is converted into scores using Positional Aggregation based Scoring (PAS) technique. The linguistic quantifiers are applied over these scores and the value of three linguistic quantifiers, ‘at least half’, ‘most’ and ‘as many as possible’, are compared with amazon ranking, evaluated on the basis of ranks explicitly taken from experts. P@10, FPR@10 and Mean Average Precision (MAP) are evaluated. It is evident from the results that quantifier ‘at least half’ outperformed others in the aforementioned performance metric. It is envisaged that the proposed approach will help the research community in designing the recommender systems to explore the relevant books and meet the expectation of the users in a better way.