基于知识图谱和读者画像的图书推荐研究

Recommending Books Based on Knowledge Graph and Reader Profiling

[目的]将知识图谱和读者画像技术应用于图书推荐,针对数据稀疏和冷启动问题,提高图书推荐的精准度。[应用背景]应用于浙江工业大学图书馆管理系统,涵盖2020年5月-2022年5月的借阅数据,包含220 636条借阅记录,60 162本图书、15 916位读者。[方法]构建读者-图书知识图谱,结合图书主题模型和读者画像分别对图书之间的语义关联和读者偏好进行建模,挖掘读者-读者、读者-图书以及图书-图书背后的语义关联,针对性地改善数据稀疏和冷启动问题。[结果]实验结果表明,相较于对比的协同过滤算法,本文方法(基于GraphSAGE)在精准率指标上提升0.151,且在冷启动环境下的召回率达到51.44%。[结论]基于知识图谱和读者画像的图书检索技术能有效改善数据稀疏和冷启动问题,具有较好的应用前景。

[Objective]This paper combines the knowledge graph and reader profiling technology to address the data sparseness and cold start issues of book recommendation.[Context]We examined the proposed model with the library management system of Zhejiang University of Technology,including 220,636 circulation records from May 2020 to May 2022.A total of 60,162 books and 15,916 readers were included in this study.[Methods]First,we constructed a reader-book knowledge graph.Then,we modeled semantic associations between books and reader preferences utilizing book theme modeling and reader profiling.Finally,we explored semantic connections among reader-reader,reader-book,and book-book relationships,strategically addressing data sparsity and cold start challenges.[Results]The proposed method based on GraphSAGE improved the precision by 0.151 compared to the existing collaborative filtering algorithm.Its recall rate reached 51.44%in the cold start environment.[Conclusions]The book recommendation method based on knowledge graph and reader portraits can effectively improve the data sparseness and cold start problem.