Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/Significance] Considering the dynamic changes of users’ interests and social relationships, this paper proposes a scholar recommendation model integrating users’ dynamic interests and social relationships. [Method/Process] Firstly, using the periodical literature of different disciplines as the classified corpus, the discipline domain of scholars’ blog posts was distinguished based on the labeled LDA model. Then KNN algorithm was used to classify blogs by discipline. At the same time, the change rate of subject interests was used to improve the time factor, and the dynamic interest similarity of scholars was calculated. The PageRank of scholars was calculated by using the quantitative relationship of links between scholars, and the global trust level was calculated by combining the PageRank and time value of blogs sent by scholars. Time weight was introduced into scholars’ comments and recommendation interaction behaviors to calculate scholars’ interactive trust level. The dynamic social trust level of scholars was obtained by integrating the global trust level and interactive trust level. Finally, the similarity of interest and trust were combined to recommend scholars. [Result/Conclusion] The scholar recommendation model integrating users’ dynamic interests and social relationships in the virtual academic community can effectively improve the quality of scholar recommendation from the perspectives of dynamic interests and dynamic social relationships.
Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] Constructing a multi-source document metadata fusion model will help improve the overall quality of document metadata, promote metadata management and utilization in the resource discovery system, and optimize user resource discovery service experience. In view of the document metadata duplication judgment strategy proposed by the writers before, this paper optimizes the strategy from experience-oriented to automated, and improves the automation level in the whole process on the premise of guaranteeing the duplication judgment and fusion effect.[Method/process] The metadata items of different types of documents were different, and the metadata items of the same document from different sources were different, which will make the method of judging duplication different. An automatic multi-source document metadata fusion model based on the decision tree was proposed, which transformed a duplication judgment problem into a classification problem. This paper selected features according to feature similarity and constructed the decision tree, on this basis, it implemented metadata duplication judgment and fusion, and took different types of document resource metadata as examples to conduct experiments to verify the effectiveness of the strategy.[Result/conclusion] The results show that for the five document types of metadata, the accuracy of the duplication judgment strategy is more than 99%, and the recall rate is more than 98%. The overall effect is good. Judgment on the effect of the fusion strategy, the quality improvement ratios of the metadata items of patents, dissertations, journal papers, conference papers and books are 15.15%, 36.80%, 15.29%, 52.63% and 15.38% respectively, all of which have significant improvement.
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