Personalized Reading Recommendations for Saccharomyces Genome Database

The rapid growth of research in biology, and the increasing degree to which different subareas of biology are connected, make it difficult to monitor the published literature effectively. To address this problem, we develope a reading recommendation system that requires no other input from users except their reading or citation history. This frees the users from the problem of expressing their information need using query languages. We use a graph representation for publication databases with rich metadata. With this representation, a path constrained random walk (PCRW) model is trained to discover effective recommendation strategies represented as edge paths on the graph. Experiments on both citation-based and history-based reading recommendation tasks show that by leveraging rich context information the PCRW-based approach outperforms random walk with restart based approaches as well as traditional content-based and collaborative filtering approaches. An online recommendation system for Saccharomyces Genome Database is available at REMOVED FOR BLIND REVIEW .

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