TY - GEN
T1 - With a little help from my friends
T2 - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011
AU - Pera, Maria Soledad
AU - Ng, Yiu Kai
PY - 2011
Y1 - 2011
N2 - 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 users on a social website, such as LibraryThing, 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) determines how 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 LibraryThing.
AB - 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 users on a social website, such as LibraryThing, 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) determines how 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 LibraryThing.
KW - Personalization
KW - Recommendation
KW - Word-similarity
UR - http://www.scopus.com/inward/record.url?scp=80155213464&partnerID=8YFLogxK
U2 - 10.1109/WI-IAT.2011.9
DO - 10.1109/WI-IAT.2011.9
M3 - Conference contribution
AN - SCOPUS:80155213464
SN - 9780769545134
T3 - Proceedings - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011
SP - 96
EP - 99
BT - Proceedings - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011
Y2 - 22 August 2011 through 27 August 2011
ER -