基于作者偏好和异构信息网络的科技文献推荐方法研究

Recommending Scientific Literature Based on Author Preference and Heterogeneous Information Network

【目的】采用异构信息网络理论和作者偏好,提高科技文献推荐质量。【方法】基于异构信息网络理论,提出一种可以融合多语义信息的科技文献推荐方法。首先,结合作者偏好信息为科技文献异构信息网络中的元路径加权;其次,采用DPRel算法计算作者与文献之间的相关度。在此基础上,构建加权作者-文献矩阵,按相关度降序排列得到推荐列表。【结果】从Web of Science中收集实验数据集,实验结果表明,在三个数据集中所提方法相较于基于单条元路径计算作者-文献相关度的推荐方法在平均成功推荐率上分别提高了6%、8%、6%,并且文献成功推荐提高率分别为14.8%、27.6%、13.0%。【局限】在数据预处理阶段由人工进行关键词统一,对于海量数据,人工处理关键词不现实。【结论】所提推荐方法提高了异构信息网络中科技文献推荐的质量。

[Objective] This study uses heterogeneous information network and author preference to improve the performance of scientific literature recommendation. [Methods] We proposed a new method using various semantic information. Firstly, we weighted the meta path in the heterogeneous information network of the scientific literature with the help of the author preference. Secondly, we used the DPRel algorithm to calculate the correlation between the author and the literature. Finally, we constructed the weighted author-literature matrix, and retrieved the recommendation list based on the descending order of the correlation. [Results] We examined our model with data sets from the Web of Science. Compared with the methods of single meta path, the average successful recommendation rate of the new algorithm was 6%, 8% and 6% higher in three datasets. The improvement rate of successful recommendation was 14.8%, 27.6% and 13.0%, respectively. [Limitations] In data preprocessing stage, the keywords were unified manually, which is unrealistic for massive data sets.[Conclusions] The proposed method could effectively improve the quality of scientific literature recommendation.