Abstract

Most existing recommendation research has been concentrated on unidirectional recommendation, i.e., only recommending items to users. However, the platform needs to achieve bi-directional recommendation in many real-world scenarios. For example, in an online recruitment scenario, the recommender system not only needs to recommend positions to candidates, but also recommend candidates to enterprises. In this paper, we develop a new bi-directional recommendation model for online recruitment termed (BiMuF) Bi-directional recommendation with Multi-semantic Filter. In BiMuF, an encoder component is utilized to learn the text embeddings, a multi-semantic filter component is designed to capture important graph representation, and a graph learning component is designed to learn the graph embeddings. In addition, a multi-task learning framework is designed to achieve bi-directional recommendations. In the multi-task learning framework, we share text embeddings and graph embeddings to alleviate the problems of data sparsity, data asymmetry, and feature generalization in online recruitment. Moreover, we conduct three new datasets based on a technology supply and demand docking platform namely South China Technology Commercialization Center, and three new datasets in job recruitment scenario namely Computer Technology-Related Job Recruitment. Extensive experiments in the real-world tasks show that BiMuF outperforms the state-of-the-art methods, verifying the effectiveness of the designs of our model. The code and the latter datasets are available at https://github.com/allminerlab.

中文翻译：

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BiMuF：用于在线招聘的具有多语义过滤器的双向推荐系统

摘要

大多数现有的推荐研究都集中在单向推荐，即仅向用户推荐项目。然而，该平台需要在许多现实场景中实现双向推荐。例如，在在线招聘场景中，推荐系统不仅需要向候选人推荐职位，还需要向企业推荐候选人。在本文中，我们开发了一种新的在线招聘双向推荐模型，称为（BiMuF）具有多语义过滤器的双向推荐。在 BiMuF 中，编码器组件用于学习文本嵌入，多语义过滤器组件用于捕获重要的图表示，图学习组件用于学习图嵌入。此外，还设计了多任务学习框架来实现双向推荐。在多任务学习框架中，我们共享文本嵌入和图嵌入，以缓解在线招聘中的数据稀疏、数据不对称和特征泛化问题。此外，我们基于技术供需对接平台“华南技术商业化中心”建立了三个新数据集，以及职位招聘场景中的三个新数据集“计算机技术相关职位招聘”。现实世界任务中的大量实验表明 BiMuF 优于最先进的方法，验证了我们模型设计的有效性。代码和后面的数据集可在 https://github.com/allminerlab 获取。