A music recommendation system based on music data grouping and user interests
Proceedings of the tenth international conference on Information and knowledge management
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Edition)
Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Edition)
IEEE Transactions on Knowledge and Data Engineering
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
Automatic record linkage using seeded nearest neighbour and support vector machine classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Individual and group behavior-based customer profile model for personalized product recommendation
Expert Systems with Applications: An International Journal
Search Engines: Information Retrieval in Practice
Search Engines: Information Retrieval in Practice
CARES: a ranking-oriented CADAL recommender system
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
A sophisticated library search strategy using folksonomies and similarity matching
Journal of the American Society for Information Science and Technology
Predicting social-tags for cold start book recommendations
Proceedings of the third ACM conference on Recommender systems
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
Personalized online video recommendation by neighborhood score propagation based global ranking
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Document recommendation in social tagging services
Proceedings of the 19th international conference on World wide web
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
Speak the same language with your friends: augmenting tag recommenders with social relations
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Social media recommendation based on people and tags
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
User comments for news recommendation in social media
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
International Journal of Approximate Reasoning
Using word clusters to detect similar web documents
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
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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.