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.
Proceedings of the 20th ACM international conference on Information and knowledge management, page 2133--2136. New York, NY, USA, ACM, (2011)
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