Exploiting the wisdom of social connections to make personalized recommendations on scholarly articles

Existing scholarly publication recommenders were designed to aid researchers, as well as ordinary users, in discovering pertinent literature in diverse academic fields. These recommenders, however, often (i) depend on the availability of users' historical data in the form of ratings or access patterns, (ii) generate recommendations pertaining to users' (articles included in their) profiles, as oppose to their current research interests, or (iii) fail to analyze valuable user-generated data at social sites that can enhance their performance. To address these design issues, we propose PReSA, a personalized recommender on scholarly articles. PReSA recommends articles bookmarked by the connections of a user U on a social bookmarking site that are not only similar in content to a target publication P currently of interest to U but are also popular among U's connections. PReSA (i) relies on the content-similarity measure to identify potential academic publications to be recommended and (ii) uses only information readily available on popular social bookmarking sites to make recommendations. Empirical studies conducted using data from CiteULike have verified the efficiency and effectiveness of (the recommendation and ranking strategies of) PReSA, which outperforms a number of existing (scholarly publication) recommenders.