[Purpose/Significance] Scientific and technological literature recommendation system is an important bridge between scientific researchers and scientific and technological literature. It serves as an update for scientific researchers to provide effective information acquisition guidance for researchers facing incremental scientific and technological resource overload. In order to solve the problem of sparse connections between literature in the recommendation of scientific and technological literature, it is necessary to introduce background knowledge and bury the high-level knowledge correlation between literature. [Method/Process] This article comprehensively utilized Wikidata and entity linking technology to construct a knowledge graph. Based on this, it combined knowledge graph embedding, attention mechanism, and multi-layer perceptron to construct a scientific and technological literature recommendation model that integrated background knowledge. Its effectiveness was verified using the CiteULike dataset. [Result/Conclusion] The experimental results show that the constructed recommendation model achieved optimal results in AUC, MRR and nDCG@5 metrics. Through case analysis, it finds that background knowledge based on knowledge graphs can effectively explore high-order knowledge associations between user interest and candidate literature, improving recommendation performance.
Zhang Guobiao
,
Li Jie
,
Wang Shaxin
,
Zhou Yi
. Construction and Empirical Study of Scientific and Technological Literature Recommendation Model Based on Knowledge Graph[J]. Library and Information Service, 2025
, 69(10)
: 125
-135
.
DOI: 10.13266/j.issn.0252-3116.2025.10.011
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