摘要：目的针对现有职位推荐中存在的大规模应用难度、冷启动、缺乏新颖性和可解释性问题提出了一种基于人才知识图谱推理的强化学习可解释推荐方法。方法在实际简历数据集基础上构建了一个人才社交经验知识图谱并基于强化学习理论在知识图谱上训练了一个策略代理。将推荐过程分解为方向选择和节点选择两个子过程使代理能够在知识图谱上搜索潜在的高质量推荐目标。结果与LR、BPRJRL-int、JRL-rep和PGPR模型相

Abstract: In response to the difficulties of large-scale application, cold start, lack of novelty, and interpretability in existing job recommendation systems, a reinforcement learning interpretable recommendation method based on talent knowledge graph reasoning is proposed. Based on actual resume datasets, a talent social experience knowledge graph is constructed, and a policy agent is trained on the knowledge graph based on reinforcement learning theory. The recommendation process is decomposed into direction selection and node selection, enabling the agent to search for potential high-quality recommendation targets on the knowledge graph. Compared with LR, BPR.JRL-int, JRL-rep, and PGPR models, the reinforcement learning interpretable recommendation model based on talent knowledge graph reasoning performs best in MRR@20 (81.7%), Hit@1 (74.8%), Hit@5 (92.2%), and Hit@10 (97.0%). The limitations of the experimental dataset size and task type are relatively limited. The model effectively combines the historical work experience of talents and recommends based on similar work experience, while incorporating the correlation of job attributes in the knowledge graph. In addition to providing recommendation results, it also provides reasoning paths, which can effectively address the problems of cold start, lack of novelty, and interpretability. Keywords: job recommendation, knowledge graph reasoning, reinforcement learning interpretable recommendation