面向LinkedIn人才搜索的深度和表示学习
Towards Deep and Representation Learning for Talent Search at LinkedIn
摘要：
LinkedIn的人才搜索和推荐系统努力将潜在候选人与招聘人员或招聘经理的招聘需求相匹配，这些需求以搜索查询或职位发布的形式表达。最近在这一领域的工作主要集中在线性模型，它不考虑功能之间的复杂关系，以及集成树模型，引入非线性，但仍然不足以探索所有潜在的功能相互作用，并严格分离的功能生成建模。在本文中，我们展示了我们在LinkedIn Recruiter上应用深度和表示学习模型的结果。我们的主要贡献包括：（i）学习人才搜索领域内稀疏实体的语义表示，例如招聘人员ID、候选人ID和技能实体ID，为此我们利用了利用LinkedIn经济图的神经网络模型，以及（ii）用于学习人才搜索应用中招聘人员参与度和候选人响应的深度模型。我们还探索了应用于深度模型的学习排序方法，并展示了人才搜索用例的好处。最后，我们展示了LinkedIn人才搜索和推荐系统的离线和在线评估结果，并讨论了在实现完全深度模型架构的道路上可能面临的挑战。所讨论的挑战和方法适用于任何多方面的搜索引擎。
英文摘要：
Talent search and recommendation systems at LinkedIn strive to match the potential candidates to the hiring needs of a recruiter or a hiring manager expressed in terms of a search query or a job posting. Recent work in this domain has mainly focused on linear models, which do not take complex relationships between features into account, as well as ensemble tree models, which introduce non-linearity but are still insufficient for exploring all the potential feature interactions, and strictly separate feature generation from modeling. In this paper, we present the results of our application of deep and representation learning models on LinkedIn Recruiter. Our key contributions include: (i) Learning semantic representations of sparse entities within the talent search domain, such as recruiter ids, candidate ids, and skill entity ids, for which we utilize neural network models that take advantage of LinkedIn Economic Graph, and (ii) Deep models for learning recruiter engagement and candidate response in talent search applications. We also explore learning to rank approaches applied to deep models, and show the benefits for the talent search use case. Finally, we present offline and online evaluation results for LinkedIn talent search and recommendation systems, and discuss potential challenges along the path to a fully deep model architecture. The challenges and approaches discussed generalize to any multi-faceted search engine.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
