Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Search Engines: Information Retrieval in Practice
Search Engines: Information Retrieval in Practice
Using word clusters to detect similar web documents
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
A group recommender for movies based on content similarity and popularity
Information Processing and Management: an International Journal
What to read next?: making personalized book recommendations for K-12 users
Proceedings of the 7th ACM conference on Recommender systems
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With the large amount of books available nowadays, users are overwhelmed with choices when they attempt to find books of interest. While existing book recommendation systems, which are based on either collaborative filtering, content-based, or hybrid methods, suggest books (among the millions available) that might be appealing to the users, their recommendations are not personalized enough to meet users' expectations due to their collective assumption on group preference and/or exact content matching, which is a failure. To address this problem, we have developed PReF, a Personalized Recommender that relies on Friendships established by user son a social website, such as Library Thing, to make book recommendations tailored to individual users. In selecting books to be recommended to a user U, who is interested in a book B, PReF (i) considers books belonged to U's friends, (ii) applies word-correlation factors to disclose books similar in contents to B, (iii)depends on the ratings given to books by U's friends to identify highly-regarded books, and (iv) determine show reliable individual friends of U are in providing books from their own catalogs (that are similar in content to B)to be recommended. We have conducted an empirical study and verified that (i) relying on data extracted from social websites improves the effectiveness of book recommenders and (ii) PReF outperforms the recommenders employed by Amazon and Library Thing.