TY - GEN
T1 - Personalized book recommendations created by using social media data
AU - Pera, Maria Soledad
AU - Condie, Nicole
AU - Ng, Yiu Kai
PY - 2011
Y1 - 2011
N2 - Book recommendation systems can benefit commercial websites, social media sites, and digital libraries, to name a few, by alleviating the knowledge acquisition process of users who look for books that are appealing to them. Even though existing book recommenders, which are based on either collaborative filtering, text content, or the hybrid approach, aid users in locating books (among the millions available), 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 PBRecS, a book recommendation system that is based on social interactions and personal interests to suggest books appealing to users. PBRecS relies on the friendships established on a social networking site, such as LibraryThing, to generate more personalized suggestions by including in the recommendations solely books that belong to a user's friends who share common interests with the user, in addition to applying word-correlation factors for partially matching book tags to disclose books similar in contents. The conducted empirical study on data extracted from LibraryThing has verified (i) the effectiveness of PBRecS using social-media data to improve the quality of book recommendations and (ii) that PBRecS outperforms the recommenders employed by Amazon and LibraryThing.
AB - Book recommendation systems can benefit commercial websites, social media sites, and digital libraries, to name a few, by alleviating the knowledge acquisition process of users who look for books that are appealing to them. Even though existing book recommenders, which are based on either collaborative filtering, text content, or the hybrid approach, aid users in locating books (among the millions available), 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 PBRecS, a book recommendation system that is based on social interactions and personal interests to suggest books appealing to users. PBRecS relies on the friendships established on a social networking site, such as LibraryThing, to generate more personalized suggestions by including in the recommendations solely books that belong to a user's friends who share common interests with the user, in addition to applying word-correlation factors for partially matching book tags to disclose books similar in contents. The conducted empirical study on data extracted from LibraryThing has verified (i) the effectiveness of PBRecS using social-media data to improve the quality of book recommendations and (ii) that PBRecS outperforms the recommenders employed by Amazon and LibraryThing.
UR - http://www.scopus.com/inward/record.url?scp=80053456864&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24396-7_31
DO - 10.1007/978-3-642-24396-7_31
M3 - Conference contribution
AN - SCOPUS:80053456864
SN - 9783642243950
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 390
EP - 403
BT - Web Information Systems Engineering - WISE 2010 Workshops - WISE 2010 International Symposium WISS and International Workshops CISE, MBC, Revised Selected Papers
T2 - Workshops on Web Information Systems Engineering, WISE 2010: 1st International Symposium on Web Intelligent Systems and Services, WISS 2010, 2nd International Workshop on Mobile Business Collaboration, MBC 2010 and 1st Int. Workshop on CISE 2010
Y2 - 12 December 2010 through 14 December 2010
ER -