Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Recommending scientific articles using citeulike
Proceedings of the 2008 ACM conference on Recommender systems
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Technical paper recommendation: a study in combining multiple information sources
Journal of Artificial Intelligence Research
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
A study of heterogeneity in recommendations for a social music service
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Using word clusters to detect similar web documents
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
IEEE Transactions on Knowledge and Data Engineering
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Researchers, as well as ordinary users who seek information in diverse academic fields, turn to the web to search for publications of interest. Even though scholarly publication recommenders have been developed to facilitate the task of discovering literature pertinent to their users, they (i) are not personalized enough to meet users' expectations, since they provide the same suggestions to users sharing similar profiles/preferences, (ii) generate recommendations pertaining to each user's general interests as opposed to the specific need of the user, and (iii) fail to take full advantages of valuable user-generated data at social websites that can enhance their performance. To address these problems, we propose PubRec, a recommender that suggests closely-related references to a particular publication P tailored to a specific user U, which minimizes the time and efforts imposed on U in browsing through general recommended publications. Empirical studies conducted using data extracted from CiteULike (i) verify the efficiency of the recommendation and ranking strategies adopted by PubRec and (ii) show that PubRec significantly outperforms other baseline recommenders.