A Personalized Method of Literature Recommendation Based on Brain Informatics Provenances

Systematic Brain Informatics (BI) depends on a lot of prior knowledge, from experimental design to result interpretation. Scientific literatures are a kind of important knowledge source. However, it is difficult for researchers to find really useful references from a large number of literatures. This paper proposes a personalized method of literature recommendation based on BI provenances. By adopting the interest retention model, user models can be built based on the Data-Brain and BI provenances. Furthermore, semantic similarity is added into traditional literature vector modeling for obtaining literature models. By measuring similarity between the user models and literature models, the really needed literatures can be obtained. Results of experiments show that the proposed method can effectively realize a personalized literature recommendation according to BI researchers' interests.