[Purpose/significance] This paper proposes a recommendation algorithm based on user interest metrics and content analysis for the current issues of low personalization and poor recommendation in knowledge discovery services. [Method/process] Through characteristic word distribution, LDA topic distribution and citation association, this paper constructs the academic resource model. Through the measurement of user behavior (browsing time, downloading, forwarding, collecting, etc.), the user's interest in browsing academic resources can be calculated, and the user interest model is constructed. Matching the user interest model with the academic resource model and calculating its similarity, the user's interest value for each academic resource can be obtained. Finally, the TOP-N academic resources with the highest interest value can be recommended to the user. [Result/conclusion] The paper tests the effectiveness of the algorithm and the accuracy of the recommendation through experiments. From the experimental results, we can show that the recommendation algorithm can predict the user's interest better and the recommendation effect is significant, simultaneously providing ideas for precise recommendation of discovery services.
Ding Mengxiao
,
Bi Qiang
,
Xu Pengcheng
,
Li Jie
,
Mu Dongmei
. Research on Precise Recommendation of Knowledge Discovery Services Based on Users Interests[J]. Library and Information Service, 2019
, 63(3)
: 21
-29
.
DOI: 10.13266/j.issn.0252-3116.2019.03.003
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