As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
Collaborative filtering-based recommender systems have been popular for Internet users as they are helpful in searching for useful information promptly. These systems rely on similarity measures to obtain recommendations from other users based on their rating history. This paper addresses the problem of vagueness and subjectivity in user ratings. To relieve this problem, we adopt the fuzzy logic to transform ratings and then compute similarity using the fuzzified ratings. Performance of the proposed method is investigated to find that it significantly outperforms existing similarity measures using hard user ratings in terms of prediction accuracy, especially with a sparse and short-ranged dataset.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.