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.
@cite_11: Background We present a probabilistic topic-based model for content similarity called pmra that underlies the related article search feature in PubMed. Whether or not a document is about a particular topic is computed from term frequencies, modeled as Poisson distributions. Unlike previous probabilistic retrieval models, we do not attempt to estimate relevance–but rather our focus is "relatedness", the probability that a user would want to examine a particular document given known interest in another. We also describe a novel technique for estimating parameters that does not require human relevance judgments; instead, the process is based on the existence of MeSH ® in MEDLINE ®.
PubMed generates related research for articles in their catalogue. They trained a probabilistic topic-based model to learn article relatedness based on article meta-data (e.g. title, abstract and MeSH terms). In testing on the TREC 2005 genomics track (including MEDLINE abstracts), the probabilistic topic-based model is shown to slightly outperform BM25, with 0.399 vs. 0.383 precision at 5, respectively @cite_11 . By taking a random week long sample of logs, PubMed show that out of two million user sessions, 19