基于用户兴趣度量的知识发现服务精准推荐

目的/意义]针对当前知识发现服务中存在的个性化程度不高和推荐效果不佳等问题，提出一种基于用户兴趣度量和内容分析的推荐算法。方法/过程]文章通过特征词分布、LDA主题分布、引文结构网络三个维度构建学术资源模型，并通过对用户行为的度量，计算用户对其浏览学术资源的兴趣度，结合学术资源模型构建用户兴趣模型。将用户兴趣模型与学术资源模型匹配，计算其相似度，得到用户对每条学术资源的兴趣值，最后将兴趣值最高的TOP-N学术资源推荐给用户。结果/结论]通过实验检验算法的有效性和推荐准确率，结果显示，本文从实时动态度量兴趣的角度，提出的推荐算法能较好地预测用户兴趣，推荐效果显著，为实现发现服务精准推荐提供思路。

Research on Precise Recommendation of Knowledge Discovery Services Based on Users Interests

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.