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A Personalized Literature Recommendation Method Based on the Domain-Driven User Interest Model

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DOI: 10.23977/icamcs.2017.1032


Sheng Wenjin, Yan Jianzhuo, Chen Jianhui, Lv Ruying, Kuai Hongzhi

Corresponding Author

Sheng Wenjin


At present, literature is the most important source of scientific knowledge. However, in the face of the information overload caused by the explosive growth of scientific literatures, it is difficult for researchers to find the literature that is really needed quickly. This is particularly acute in the current universal concern field of cognitive science. This paper forms a personalized literature recommendation method based on domain–driven user interest model. By adopting the recommendation method based on BI provenances, initial literatures can be acquired from the first recommendation module. Furthermore, spread activation theory is added into the second recommendation module for obtaining user interest model. Results of experiments show that the proposed method can make full use of the advantages of the two modules, which can not only recommend literatures that relevant to the user’s research interests but also recommend literatures that in other relevant heat research domain.


literature recommendation, cognitive science, spread activation theory, domain-driven user interest model

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