用时空图神经网络解决招聘推荐中的冷启动和时间敏感问题
TIMBRE: Efficient Job Recommendation On Heterogeneous Graphs For Professional Recruiters
- 构建异构图融合用户与职位信息，引入时间维度建模
- 在真实数据集上显著优于传统协同过滤方法
- 适合需要精准时序推荐的招聘平台开发者
职位推荐面临诸多挑战：用户（求职者）与职位（物品）生命周期短，导致表示学习困难；时间因素至关重要，不能推荐过早或过晚的职位；仅依赖历史交互不可靠。本文提出一种基于时间的异构图推荐方法——TIMBRE（Temporal Integrated Model for Better REcommendations），将用户与职位信息整合到异构图中，并设计支持高效时序推荐与评估的图结构，最终通过图神经网络实现推荐。实验采用推荐系统常用指标，在图基推荐系统中罕见地进行了全面评估。
原文摘要 · Abstract (English)
Job recommendation gathers many challenges well-known in recommender systems. First, it suffers from the cold start problem, with the user (the candidate) and the item (the job) having a very limited lifespan. It makes the learning of good user and item representations hard. Second, the temporal aspect is crucial: We cannot recommend an item in the future or too much in the past. Therefore, using solely collaborative filtering barely works. Finally, it is essential to integrate information about the users and the items, as we cannot rely only on previous interactions. This paper proposes a temporal graph-based method for job recommendation: TIMBRE (Temporal Integrated Model for Better REcommendations). TIMBRE integrates user and item information into a heterogeneous graph. This graph is adapted to allow efficient temporal recommendation and evaluation, which is later done using a graph neural network. Finally, we evaluate our approach with recommender system metrics, rarely computed on graph-based recommender systems.
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