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| Recommending Books Based on Knowledge Graph and Reader Profiling |
Chen Linghong1( ),Pan Xiaohua2 |
1Zhejiang University of Technology Library, Hangzhou 310014, China
2Binjiang Institute of Zhejiang University, Hangzhou 310053, China |
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Abstract [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.
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Received: 10 October 2022
Published: 30 March 2023
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| Fund:Humanities and Social Sciences Research of the Ministry of Education(17YJA870003) |
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Corresponding Authors:
Chen Linghong，ORCID：0000-0003-3891-2059，E-mail：23788594@qq.com。
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| [1] |
丁雪. 基于数据挖掘的图书智能推荐系统研究[J]. 情报理论与实践, 2010, 33(5): 107-110.
|
| [1] |
(Ding Xue. Research on the Book Intelligent Recommendation System Based on Data Mining[J]. Information Studies: Theory & Application, 2010, 33(5): 107-110.)
|
| [2] |
Schafer J B, Frankowski D, Herlocker J, et al. Collaborative Filtering Recommender Systems[AM]//The Adaptive Web: Methods and Strategies of Web Personalization[M]. Berlin, Heidelberg: Springer, 2007: 291-324.
|
| [3] |
Mooney R J, Roy L. Content-Based Book Recommending Using Learning for Text Categorization[C]// Proceedings of the 5th ACM Conference on Digital Libraries. 2000: 195-204.
|
| [4] |
徐海文, 谭台哲. 基于知识图谱的个性化推荐系统构建[J]. 数字技术与应用, 2022, 40(3): 152-154, 164.
|
| [4] |
(Xu Haiwen, Tan Taizhe. Construction of Personalized Recommendation System Based on Knowledge Map[J]. Digital Technology & Application, 2022, 40(3): 152-154, 164.)
|
| [5] |
王大阜, 邓志文, 贾志勇, 等. 基于用户画像的高校图书馆个性化图书推荐研究[J]. 河南师范大学学报 (自然科学版), 2022, 50(3): 95-103.
|
| [5] |
(Wang Dafu, Deng Zhiwen, Jia Zhiyong, et al. Research on Personalized Book Recommendation of University Library Based on User Profile[J]. Journal of Henan Normal University(Natural Science Edition), 2022, 50(3): 95-103.)
|
| [6] |
Zhu Y C, Xie R B, Zhuang F Z, et al. Learning to Warm Up Cold Item Embeddings for Cold-Start Recommendation with Meta Scaling and Shifting Networks[C]// Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021: 1167-1176.
|
| [7] |
宋楚平. 一种改进的协同过滤方法在高校图书馆图书推荐中的应用[J]. 图书情报工作, 2016, 60(24): 86-91.
doi: 10.13266/j.issn.0252-3116.2016.24.012
|
| [7] |
(Song Chuping. Application of an Improved Collaborative Filtering Method on Recommending Books in College Libraries[J]. Library and Information Service, 2016, 60(24): 86-91.)
doi: 10.13266/j.issn.0252-3116.2016.24.012
|
| [8] |
江周峰, 鄂海红, 杨俊. 基于时间上下文信息的借阅次数评分模型与应用[J]. 图书情报工作, 2014, 58(S2): 220-223.
|
| [8] |
(Jiang Zhoufeng, E Haihong, Yang Jun. A Scoring Model of Borrowing Times Based on Time Context Information and Its Application[J]. Library and Information Service, 2014, 58(S2): 220-223.)
|
| [9] |
毛志勇, 赵盼盼. 基于云填充和蚁群聚类的协同过滤图书推荐算法[J]. 现代情报, 2015, 35(5): 78-82.
doi: 10.3969/j.issn.1008-0821.2015.05.015
|
| [9] |
(Mao Zhiyong, Zhao Panpan. A Collaborative Filtering Recommendation Algorithm Based on Cloud Model and Ants Clustering[J]. Journal of Modern Information, 2015, 35(5): 78-82.)
doi: 10.3969/j.issn.1008-0821.2015.05.015
|
| [10] |
董坤. 基于协同过滤的学科信息服务平台构建[J]. 情报理论与实践, 2012, 35(5): 77-80.
|
| [10] |
(Dong Kun. Construction of the Subject Information Service Platform Based on Collaborative Filtering[J]. Information Studies: Theory & Application, 2012, 35(5): 77-80.)
|
| [11] |
赵衍, 杨喆涵. 基于知识图谱的我国高校图书馆个性化推荐研究综述[J]. 上海管理科学, 2021, 43(5): 116-124.
|
| [11] |
(Zhao Yan, Yang Zhehan. Literature Review of the Research on Personalized Recommendation of Chinese University Libraries Based on Knowledge Graph[J]. Shanghai Management Science, 2021, 43(5): 116-124.)
|
| [12] |
吴荣, 段宏涛. 知识图谱在图书馆推荐系统中的应用研究[J]. 自动化应用, 2020(8): 71-73, 76.
|
| [12] |
(Wu Rong, Duan Hongtao. Research on the Application of Knowledge Graph in Library Recommendation System[J]. Automation Application, 2020(8): 71-73, 76.)
|
| [13] |
Zhao H, Yao Q M, Li J D, et al. Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks[C]// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017: 635-644.
|
| [14] |
Bordes A, Usunier N, Garcia-Duran A, et al. Translating Embeddings for Modeling Multi-Relational Data[C]// Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. 2013: 2787-2795.
|
| [15] |
Wang Z, Zhang J W, Feng J L, et al. Knowledge Graph Embedding by Translating on Hyperplanes[C]// Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014.
|
| [16] |
Lin Y K, Liu Z Y, Sun M S, et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion[C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015: 2181-2187.
|
| [17] |
Ji G L, He S Z, Xu L H, et al. Knowledge Graph Embedding via Dynamic Mapping Matrix[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1:Long Papers). 2015: 687-696.
|
| [18] |
Hamilton W L, Ying R, Leskovec J. Inductive Representation Learning on Large Graphs[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017: 1025-1035.
|
| [19] |
Grover A, Leskovec J. Node2Vec: Scalable Feature Learning for Networks[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 855-864.
|
| [20] |
刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3): 582-600.
|
| [20] |
(Liu Qiao, Li Yang, Duan Hong, et al. Knowledge Graph Construction Techniques[J]. Journal of Computer Research and Development, 2016, 53(3): 582-600.)
|
| [21] |
Lu Y J, Liu Q, Dai D, et al. Unified Structure Generation for Universal Information Extraction[OL]. arXiv Preprint, arXiv: 2203.12277.
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