Download pdf
An overview of knowledge graph-based recommendation systems
- 1 Chengdu Shishi Middle School
* Author to whom correspondence should be addressed.
Abstract
Recommendation systems have emerged as effective tools for mitigating information overload. Traditionally, recommendation systems employ various models such as Collaborative Filtering, Matrix Decomposition, and Logic Decomposition. Among these, Collaborative Filtering stands out due to its high efficiency. However, it encounters challenges related to cold start and sparse data. To address these challenges, the integration of Knowledge Graphs with recommendation systems has demonstrated significant advantages. This paper classifies Knowledge Graph-based recommendation systems into two categories: enhanced classical recommendation models and novel recommendation models integrated with Knowledge Graphs. We provide explanations for each category and compare them with traditional methods to draw conclusions. To inspire future research endeavors, this article identifies potential research areas and highlights unresolved issues.
Keywords
knowledge graph, recommendation system, graph neural network
[1]. B. Schafer, “LNCS 4321 - Collaborative Filtering Recommender Systems,” 2007. [Online]. Available: https://www.researchgate.net/publication/200121027
[2]. M. Elahi, F. Ricci, and N. Rubens, “LNBIP 188 - Active Learning in Collaborative Filtering Recommender Systems.” [Online]. Available: http://www.unibz.ithttp//www.uec.ac.jp
[3]. Y. Cao, X. Wang, X. He, Z. Hu, and T. S. Chua, “Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences,” in The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, Association for Computing Machinery, Inc, May 2019, pp. 151–161. doi: 10.1145/3308558.3313705.
[4]. F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W. Y. Ma, “Collaborative knowledge base embedding for recommender systems,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, Aug. 2016, pp. 353–362. doi: 10.1145/2939672.2939673.
[5]. M. Zhu, D. S. Zhen, R. Tao, Y. Q. Shi, X. Y. Feng, and Q. Wang, Top-N collaborative filtering recommendation algorithm based on knowledge graph embedding, vol. 1027. Springer International Publishing, 2019. doi: 10.1007/978-3-030-21451-7_11.
[6]. Q. Ai, V. Azizi, X. Chen, and Y. Zhang, “Learning heterogeneous knowledge base embeddings for explainable recommendation,” Algorithms, vol. 11, no. 9, Sep. 2018, doi: 10.3390/a11090137.
[7]. Y. Tian et al., “Joint Knowledge Pruning and Recurrent Graph Convolution for News Recommendation,” in SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, Inc, Jul. 2021, pp. 51–60. doi: 10.1145/3404835.3462912.
[8]. E. Palumbo, D. Monti, G. Rizzo, R. Troncy, and E. Baralis, “entity2rec: Property-specific knowledge graph embeddings for item recommendation,” Expert Syst. Appl., vol. 151, pp. 1–48, 2020, doi: 10.1016/j.eswa.2020.113235.
[9]. X. Wang, X. Liu, J. Liu, X. Chen, and H. Wu, “A novel knowledge graph embedding based API recommendation method for Mashup development,” World Wide Web, vol. 24, no. 3, pp. 869–894, 2021, doi: 10.1007/s11280-021-00894-3.
[10]. D. Kikuta et al., “KQGC : Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation”.
[11]. X. Sha, Z. Sun, and J. Zhang, “Hierarchical attentive knowledge graph embedding for personalized recommendation,” Electron. Commer. Res. Appl., vol. 48, no. June, p. 101071, 2021, doi: 10.1016/j.elerap.2021.101071.
[12]. M. Singh, “Scalability and sparsity issues in recommender datasets: a survey,” Knowl. Inf. Syst., vol. 62, no. 1, pp. 1–43, 2020, doi: 10.1007/s10115-018-1254-2.
[13]. B. M. Sarwar, Sparsity, scalability, and distribution in recommender systems. University of Minnesota, 2001.
[14]. P. Goyal and E. Ferrara, “Graph Embedding Techniques, Applications, and Performance: A Survey,” May 2017, doi: 10.1016/j.knosys.2018.03.022.
[15]. C. Yang, Z. Liu, C. Tu, C. Shi, and M. Sun, Network embedding: theories, methods, and applications. Springer Nature, 2022.
[16]. S. Bhagat, G. Cormode, and S. Muthukrishnan, “Node Classification in Social Networks,” Jan. 2011, doi: 10.1007/978-1-4419-8462-3_5.
[17]. A. M. Khattak et al., “Tweets classification and sentiment analysis for personalized tweets recommendation,” Complexity, vol. 2020, 2020, doi: 10.1155/2020/8892552.
[18]. A. Galland and M. Lelarge, “Invariant embedding for graph classification,” 2019. [Online]. Available: https://hal.science/hal-02947290
[19]. D. Liben-Nowell and J. Kleinberg, “The Link-Prediction Problem for Social Networks.” [Online]. Available: www.arxiv.org.
[20]. Y. Zhang and X. Chen, “Explainable recommendation: A survey and new perspectives,” Foundations and Trends in Information Retrieval, vol. 14, no. 1. Now Publishers Inc, pp. 1–101, Mar. 11, 2020. doi: 10.1561/1500000066.
[21]. M. I. Jordan and T. M. Mitchell, “Machine learning:Trends,perspectives,and prospects,” Science (80-. )., vol. 349, no. 6245, pp. 253–255, Jul. 2015, doi: 10.1126/science.aac4520.
[22]. S. Sun, Z. Cao, H. Zhu, and J. Zhao, “A Survey of Optimization Methods from a Machine Learning Perspective,” Jun. 2019, [Online]. Available: http://arxiv.org/abs/1906.06821
[23]. D. Liang, L. Charlin, and D. M. Blei, “Causal Inference for Recommendation.” [Online]. Available: http://arxiv.org
[24]. Y. Mao, S. A. Mokhov, and S. P. Mudur, “Application of Knowledge Graphs to Provide Side Information for Improved Recommendation Accuracy,” Jan. 2021, [Online]. Available: http://arxiv.org/abs/2101.03054
[25]. B. Yu, C. Zhou, C. Zhang, G. Wang, and Y. Fan, “A Privacy-Preserving Multi-Task Framework for Knowledge Graph Enhanced Recommendation,” IEEE Access, vol. 8, pp. 115717–115727, 2020, doi: 10.1109/ACCESS.2020.3004250.
[26]. M. F. Dacrema, P. Cremonesi, and D. Jannach, “Are we really making much progress? A worrying analysis of recent neural recommendation approaches,” in RecSys 2019 - 13th ACM Conference on Recommender Systems, Association for Computing Machinery, Inc, Sep. 2019, pp. 101–109. doi: 10.1145/3298689.3347058.
[27]. H. Peng, J. Li, Q. Gong, Y. Ning, S. Wang, and L. He, “Motif-Matching Based Subgraph-Level Attentional Convolutional Network for Graph Classification.” [Online]. Available: www.aaai.org
[28]. J. Zhao, X. Wang, C. Shi, B. Hu, G. Song, and Y. Ye, “Heterogeneous Graph Structure Learning for Graph Neural Networks,” 2021. [Online]. Available: www.aaai.org
[29]. H. Wang, Y. Deng, L. Lü, and G. Chen, “Hyperparameter-free and Explainable Whole Graph Embedding,” Aug. 2021, [Online]. Available: http://arxiv.org/abs/2108.02113
[30]. J. Gope and S. K. Jain, “A survey on solving cold start problem in recommender systems,” in Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2017, Institute of Electrical and Electronics Engineers Inc., Dec. 2017, pp. 133–138. doi: 10.1109/CCAA.2017.8229786.
[31]. Q. Xie, X. Ma, Z. Dai, and E. Hovy, “An Interpretable Knowledge Transfer Model for Knowledge Base Completion,” Apr. 2017, [Online]. Available: http://arxiv.org/abs/1704.05908
[32]. W. Zhang, B. Paudel, W. Zhang, A. Bernstein, and H. Chen, “Interaction embeddings for prediction and explanation in knowledge graphs,” in WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining, Association for Computing Machinery, Inc, Jan. 2019, pp. 96–104. doi: 10.1145/3289600.3291014.
[33]. B. Hui, L. Zhang, X. Zhou, X. Wen, and Y. Nian, “Personalized recommendation system based on knowledge embedding and historical behavior,” Appl. Intell., vol. 52, no. 1, pp. 954–966, 2022, doi: 10.1007/s10489-021-02363-w.
Cite this article
Ye,Y. (2024). An overview of knowledge graph-based recommendation systems. Applied and Computational Engineering,73,57-68.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Software Engineering and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).