Recommending Scientific Literature: Comparing Use-Cases and Algorithms
This work addresses the need for researchers to find relevant literature, but it is incremental as it combines existing methods without introducing new algorithms.
The study tackled the problem of recommending scientific publications by comparing collaborative filtering, content-based filtering, and a hybrid system across four relatedness scenarios, finding that the hybrid system achieved the best performance with up to 70% precision.
An important aspect of a researcher's activities is to find relevant and related publications. The task of a recommender system for scientific publications is to provide a list of papers that match these criteria. Based on the collection of publications managed by Mendeley, four data sets have been assembled that reflect different aspects of relatedness. Each of these relatedness scenarios reflect a user's search strategy. These scenarios are public groups, venues, author publications and user libraries. The first three of these data sets are being made publicly available for other researchers to compare algorithms against. Three recommender systems have been implemented: a collaborative filtering system; a content-based filtering system; and a hybrid of these two systems. Results from testing demonstrate that collaborative filtering slightly outperforms the content-based approach, but fails in some scenarios. The hybrid system, that combines the two recommendation methods, provides the best performance, achieving a precision of up to 70%. This suggests that both techniques contribute complementary information in the context of recommending scientific literature and different approaches suite for different information needs.