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Contextualized Knowledge Graph Embedding for Explainable Talent Training Course Recommendation

Published: 27 September 2023 Publication History

Abstract

Learning and development, or L&D, plays an important role in talent management, which aims to improve the knowledge and capabilities of employees through a variety of performance-oriented training activities. Recently, with the rapid development of enterprise management information systems, many research efforts and industrial practices have been devoted to building personalized employee training course recommender systems. Nevertheless, a widespread challenge is how to provide explainable recommendations with the consideration of different learning motivations from talents. To this end, we propose CKGE, a contextualized knowledge graph (KG) embedding approach for developing an explainable training course recommender system. A novel perspective of CKGE is to integrate both the contextualized neighbor semantics and high-order connections as motivation-aware information for learning effective representations of talents and courses. Specifically, in CKGE, for each entity pair (i.e., the talent-course pair), we first construct a meta-graph, including the neighbors of each entity and the meta-paths between entities as motivation-aware information. Then, we develop a novel KG-based Transformer, which can serialize entities and paths in the meta-graph as a sequential input, with the specially designed relational attention and structural encoding mechanisms to better model the global dependence of KG structured data. Meanwhile, the local path mask prediction can effectively reveal the importance of different paths. As a result, CKGE not only can make precise predictions but also can discriminate the saliencies of meta-paths in characterizing corresponding preferences. Extensive experiments on real-world and public datasets clearly validate the effectiveness and interpretability of CKGE compared with state-of-the-art baselines.

References

[1]
Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 6 (2005), 734–749.
[2]
Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer normalization. CoRR abs/1607.06450 (2016).
[3]
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems. 2787–2795.
[4]
Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik. 2011. Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011). In Proceedings of the ACM Conference on Recommender Systems (RecSys’11).
[5]
Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua. 2019. Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In Proceedings of the World Wide Web Conference. 151–161.
[6]
Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua. 2019. Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In Proceedings of the World Wide Web Conference. 151–161.
[7]
Chong Chen, Weizhi Ma, Min Zhang, Zhaowei Wang, Xiuqiang He, Chenyang Wang, Yiqun Liu, and Shaoping Ma. 2021. Graph heterogeneous multi-relational recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence. 3958–3966.
[8]
Yun-Nung Chen, William Yang Wang, and Alexander I. Rudnicky. 2015. Jointly modeling inter-slot relations by random walk on knowledge graphs for unsupervised spoken language understanding. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 619–629.
[9]
Louis V. DiBello, Louis A. Roussos, and William Stout. 2006. Review of cognitively diagnostic assessment and a summary of psychometric models. In Handbook of Statistics, C. R. Rao and S. Sinharay (Eds.). Vol. 26. North Holland, 979–1030.
[10]
Yuntao Du, Xinjun Zhu, Lu Chen, Ziquan Fang, and Yunjun Gao. 2022. MetaKG: Meta-learning on knowledge graph for cold-start recommendation. CoRR abs/2202.03851 (2022).
[11]
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Yihong Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph neural networks for social recommendation. In Proceedings of the World Wide Web Conference. 417–426.
[12]
Bairan Fu, Wenming Zhang, Guangneng Hu, Xinyu Dai, Shujian Huang, and Jiajun Chen. 2021. Dual side deep context-aware modulation for social recommendation. In Proceedings of the Web Conference. 2524–2534.
[13]
Li Gao, Hong Yang, Jia Wu, Chuan Zhou, Weixue Lu, and Yue Hu. 2018. Recommendation with multi-source heterogeneous information. In Proceedings of the International Joint Conference on Artificial Intelligence. 3378–3384.
[14]
Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. 2020. A survey on knowledge graph-based recommender systems. CoRR abs/2003.00911 (2020).
[15]
Zhenyu Han, Fengli Xu, Jinghan Shi, Yu Shang, Haorui Ma, Pan Hui, and Yong Li. 2020. Genetic meta-structure search for recommendation on heterogeneous information network. In Proceedings of the ACM International Conference on Information and Knowledge Management. 455–464.
[16]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770–778.
[17]
Shizhu He, Cao Liu, Kang Liu, and Jun Zhao. 2017. Generating natural answers by incorporating copying and retrieving mechanisms in sequence-to-sequence learning. In Proceedings of the Annual Meeting of the Association for Computational Linguistics. 199–208.
[18]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the World Wide Web Conference. 173–182.
[19]
Binbin Hu, Chuan Shi, Wayne Xin Zhao, and Philip S. Yu. 2018. Leveraging meta-path based context for top-n recommendation with a neural co-attention model. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1531–1540.
[20]
Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, and Edward Y. Chang. 2018. Improving sequential recommendation with knowledge-enhanced memory networks. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 505–514.
[21]
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and Philip S. Yu. 2020. A survey on knowledge graphs: Representation, acquisition and applications. CoRR abs/2002.00388 (2020).
[22]
Jim Kirkpatrick and Wendy Kayser Kirkpatrick. 2015. An Introduction to the New World Kirkpatrick Model. Kirkpatrick Partners.
[23]
Walid Krichene and Steffen Rendle. 2021. On sampled metrics for item recommendation (extended abstract). In Proceedings of the International Joint Conference on Artificial Intelligence. 4784–4788.
[24]
Yakun Li, Lei Hou, and Juanzi Li. 2023. Preference-aware graph attention networks for cross-domain recommendations with collaborative knowledge graph. ACM Transactions on Information Systems 41, 3 (2023), Article 80, 26 pages.
[25]
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the AAAI Conference on Artificial Intelligence. 2181–2187.
[26]
Yong Liu, Susen Yang, Yonghui Xu, Chunyan Miao, Min Wu, and Juyong Zhang. 2023. Contextualized graph attention network for recommendation with item knowledge graph. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2023), 181–195.
[27]
Weizhi Ma, Min Zhang, Yue Cao, Woojeong Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, and Xiang Ren. 2019. Jointly learning explainable rules for recommendation with knowledge graph. In Proceedings of the World Wide Web Conference. 1210–1221.
[28]
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: Online learning of social representations. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 701–710.
[29]
Chuan Qin, Hengshu Zhu, Tong Xu, Chen Zhu, Chao Ma, Enhong Chen, and Hui Xiong. 2020. An enhanced neural network approach to person-job fit in talent recruitment. ACM Transactions on Information Systems 38, 2 (2020), 1–33.
[30]
Yanru Qu, Ting Bai, Weinan Zhang, Jian-Yun Nie, and Jian Tang. 2019. An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation. CoRR abs/1908.04032 (2019).
[31]
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. CoRR abs/1910.10683 (2020).
[32]
Harshali Rane and Krishna Warhade. 2021. A survey on deep learning for intracranial hemorrhage detection. In Proceedings of the International Conference on Emerging Smart Computing and Informatics. IEEE, Los Alamitos, CA, 38–42.
[33]
Brijesh Sivathanu and Rajasshrie Pillai. 2018. Smart HR 4.0—How Industry 4.0 is disrupting HR. Human Resource Management International Digest 26, 4 (2018), 7–11.
[34]
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15, 1 (2014), 1929–1958.
[35]
Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S. Yu, and Tianyi Wu. 2011. PathSim: Meta path-based top-k similarity search in heterogeneous information networks. Journal of VLDB Endowment 4, 11 (2011), 992–1003.
[36]
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019. RotatE: Knowledge graph embedding by relational rotation in complex space. In Proceedings of the International Conference on Learning Representations.
[37]
Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Long-Kai Huang, and Chi Xu. 2018. Recurrent knowledge graph embedding for effective recommendation. In Proceedings of the ACM Conference on Recommender Systems. 297–305.
[38]
Juntao Tan, Shijie Geng, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Yunqi Li, and Yongfeng Zhang. 2022. Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning. In Proceedings of the World Wide Web Conference on World Wide Web. 1018–1027.
[39]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998–6008.
[40]
V. Vovk and R. Wang. 2012. Combining p-values via averaging. arXiv:1212.4966 (2012).
[41]
Chao Wang, Hengshu Zhu, Peng Wang, Chen Zhu, Xi Zhang, Enhong Chen, and Hui Xiong. 2022. Personalized and explainable employee training course recommendations: A Bayesian variational approach. ACM Transactions on Information Systems 40, 4 (2022), Article 70, 32 pages.
[42]
Chao Wang, Hengshu Zhu, Chen Zhu, Xi Zhang, Enhong Chen, and Hui Xiong. 2020. Personalized employee training course recommendation with career development awareness. In Proceedings of the World Wide Web Conference. 1648–1659.
[43]
Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, and Qi Liu. 2018. SHINE: Signed heterogeneous information network embedding for sentiment link prediction. In Proceedings of the ACM International Conference on Web Search and Data Mining. 592–600.
[44]
Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. DKN: Deep knowledge-aware network for news recommendation. In Proceedings of the World Wide Web Conference on World Wide Web. 1835–1844.
[45]
Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019. Knowledge graph convolutional networks for recommender systems. In Proceedings of the World Wide Web Conference. 3307–3313.
[46]
Quan Wang, Pingping Huang, Haifeng Wang, Songtai Dai, Wenbin Jiang, Jing Liu, Yajuan Lyu, Yong Zhu, and Hua Wu. 2019. CoKE: Contextualized knowledge graph embedding. CoRR abs/1911.02168 (2019).
[47]
Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. KGAT: Knowledge graph attention network for recommendation. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 950–958.
[48]
Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, and Tat-Seng Chua. 2021. Learning intents behind interactions with knowledge graph for recommendation. In Proceedings of the World Wide Web Conference. 878–887.
[49]
Xiangmeng Wang, Qian Li, Dianer Yu, Zhichao Wang, Hongxu Chen, and Guandong Xu. 2022. MGPolicy: Meta graph enhanced off-policy learning for recommendations. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 1369–1378.
[50]
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the AAAI Conference on Artificial Intelligence. 1112–1119.
[51]
Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, and Joemon M. Jose. 2019. Relational collaborative filtering: Modeling multiple item relations for recommendation. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 125–134.
[52]
Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep matrix factorization models for recommender systems. In Proceedings of the International Joint Conference on Artificial Intelligence. 3203–3209.
[53]
Rui Yan, Ran Le, Yang Song, Tao Zhang, Xiangliang Zhang, and Dongyan Zhao. 2019. Interview choice reveals your preference on the market: To improve job-resume matching through profiling memories. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery Data Mining. 914–922.
[54]
Mengyue Yang, Qingyang Li, Zhiwei (Tony) Qin, and Jieping Ye. 2020. Hierarchical adaptive contextual bandits for resource constraint based recommendation. In Proceedings of the Web Conference. 292–302.
[55]
Yang Yang, Jia-Qi Yang, Ran Bao, De-Chuan Zhan, Hengshu Zhu, Xiaoru Gao, Hui Xiong, and Jian Yang. 2023. Corporate relative valuation using heterogeneous multi-modal graph neural network. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2023), 211–224.
[56]
Yang Yang, Jingshuai Zhang, Fan Gao, Xiaoru Gao, and Hengshu Zhu. 2022. DOMFN: A divergence-orientated multi-modal fusion network for resume assessment. In Proceedings the 30th ACM International Conference on Multimedia. 1612–1620.
[57]
Qing Ye, Chang-Yu Hsieh, Ziyi Yang, Yu Kang, Jiming Chen, Dongsheng Cao, Shibo He, and Tingjun Hou. 2021. A unified drug–target interaction prediction framework based on knowledge graph and recommendation system. Nature Communications 12, 1 (2021), 6775.
[58]
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021. Do transformers really perform bad for graph representation? CoRR abs/2106.05234 (2021).
[59]
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 974–983.
[60]
Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, and Jiawei Han. 2014. Personalized entity recommendation: A heterogeneous information network approach. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining. 283–292.
[61]
Xiao Yu, Xiang Ren, Yizhou Sun, Bradley Sturt, Urvashi Khandelwal, Quanquan Gu, Brandon Norick, and Jiawei Han. 2013. Recommendation in heterogeneous information networks with implicit user feedback. In Proceedings of the ACM Conference on Recommender Systems. 347–350.
[62]
Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative knowledge base embedding for recommender systems. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 353–362.
[63]
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys 52, 1 (2019), Article 5, 38 pages.
[64]
Yongfeng Zhang and Xu Chen. 2020. Explainable Recommendation: A Survey and New Perspectives. Foundations and Trends in Information Retrieval. Now Publishers.
[65]
Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI Conference on Artificial Intelligence. 5941–5948.
[66]
Fan Zhu, Horace Ho-Shing Ip, Apple W. P. Fok, and Jiaheng Cao. 2007. PeRES: A personalized recommendation education system based on multi-agents & SCORM. In Advances in Web Based Learning—ICWL 2007. Lecture Notes in Computer Science, Vol. 4823. Springer, 31–42.

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  1. Contextualized Knowledge Graph Embedding for Explainable Talent Training Course Recommendation

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    cover image ACM Transactions on Information Systems
    ACM Transactions on Information Systems  Volume 42, Issue 2
    March 2024
    897 pages
    EISSN:1558-2868
    DOI:10.1145/3618075
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 September 2023
    Online AM: 08 August 2023
    Accepted: 28 April 2023
    Revised: 02 March 2023
    Received: 09 June 2022
    Published in TOIS Volume 42, Issue 2

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    Author Tags

    1. Course recommendation
    2. knowledge graph
    3. Transformer

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    • Research-article

    Funding Sources

    • National Key RD Program of China
    • NSFC
    • Natural Science Foundation of Jiangsu Province of China
    • Jiangsu Shuangchuang (Mass Innovation and Entrepreneurship) Talent Program, the Young Elite Scientists Sponsorship Program by CAST
    • Fundamental Research Funds for the Central Universities

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