基于人才知识图谱推理的强化学习可解释推荐研究

Interpretable Recommendation of Reinforcement Learning Based on Talent Knowledge Graph Reasoning

【目的】为解决现有工作推荐存在的难以大规模应用、冷启动、缺乏新颖性和解释性等问题,提出基于人才知识图谱推理的强化学习可解释推荐方法。【方法】基于真实的简历数据集构建人才社会经历知识图谱,依据强化学习的理论在知识图谱上训练一个策略智能体,将一次推理过程分解为选择方向、选择节点两个子过程,使其能够在知识图谱上寻找潜在的优质推荐目标。【结果】相比于LR、BPR、JRL-int、JRL-rep及PGPR模型,基于人才知识图谱推理的强化学习可解释推荐模型在MRR@20(81.7%)、Hit@1(74.8%)、Hit@5(92.2%)以及Hit@10(97.0%)均表现最优。【局限】实验数据集规模和任务类型相对有限。【结论】该模型有效结合人才历史工作经历、相似人才工作经历进行推荐,结合知识图谱工作岗位的属性关联,在给出推荐结果的同时,提供推理路径,能够有效应对冷启动和缺乏新颖性、可解释性问题。

[Objective]This paper proposes an interpretable reinforcement learning method for job recommendation based on talent knowledge graph reasoning,which addresses the issues of difficulties in largescale application,cold start,and lack of novelty.[Methods]First,we constructed a knowledge graph for the social experience of the job applicants based on their resume data.Then,we trained a strategic agent with the knowledge graph and the theory of reinforcement learning.This algorithm,which divided the reasoning process into choosing directions and nodes,could identify potential high-quality recommendation targets from the knowledge graph.[Results]The MRR@20(81.7%),Hit@1(74.8%),Hit@5(92.2%)and Hit@10(97.0%)of the proposed model were higher than those of the LR,BPR,JRL-int,JRL-rep and PGPR models.[Limitations]The size of the experimental datasets and the task-types needs to be further expanded.[Conclusions]Our model could effectively recommend jobs for applicants based on their previous experience or other successful recommendations.It also provides reasoning paths with the help of knowledge graph.