论文标题
Aminergnn：纸点点击率预测的异质图神经网络与融合查询
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query
论文作者
论文摘要
用用户生成的关键字的纸张建议是建议同时满足用户兴趣的论文，并且与输入关键字相关。这是一个建议任务，其中包括两个查询，也就是用户ID和关键字。但是，现有方法根据一个查询（又称用户ID）重点放在建议上，并且不适用于解决此问题。在本文中，我们提出了一个新型的点击率（CTR）预测模型，该模型使用异质图神经网络（称为Aminergnn）推荐了两个查询的论文。具体而言，Aminergnn通过图表表示学习将项目用户，纸张和关键字构建为项目用户，纸张和关键字。为了处理两个查询，一个新颖的查询融合层旨在动态地识别其重要性，然后将它们融合为一个构建统一和端到端推荐系统的查询。我们提出的数据集和在线A/B测试的实验结果证明了Aminergnn的优势。
Paper recommendation with user-generated keyword is to suggest papers that simultaneously meet user's interests and are relevant to the input keyword. This is a recommendation task with two queries, a.k.a. user ID and keyword. However, existing methods focus on recommendation according to one query, a.k.a. user ID, and are not applicable to solving this problem. In this paper, we propose a novel click-through rate (CTR) prediction model with heterogeneous graph neural network, called AMinerGNN, to recommend papers with two queries. Specifically, AMinerGNN constructs a heterogeneous graph to project user, paper, and keyword into the same embedding space by graph representation learning. To process two queries, a novel query attentive fusion layer is designed to recognize their importances dynamically and then fuse them as one query to build a unified and end-to-end recommender system. Experimental results on our proposed dataset and online A/B tests prove the superiority of AMinerGNN.
