论文标题
点击率预测的深度多代表模型
Deep Multi-Representation Model for Click-Through Rate Prediction
论文作者
论文摘要
点击率预测（CTR）是推荐系统的关键任务，并且在过去几年中引起了极大的关注。最近的研究的主要目的强调，使用各种组件（例如深神经网络（DNN），十字网或变压器块）通过挖掘低和高特征相互作用来获得有意义而强大的表示形式。在这项工作中，我们提出了深层多代表模型（DEEPMR），该模型共同训练了两个强大的功能表示学习组件的混合物，即DNNS和多头自我参与。此外，DEEPMR将与DNN的零初始化（REZERO）连接和多头自我发场成分相结合，以学习出色的输入表示。三个现实世界数据集的实验表明，在点击率预测的任务中，提出的模型大大优于所有最新模型。
Click-Through Rate prediction (CTR) is a crucial task in recommender systems, and it gained considerable attention in the past few years. The primary purpose of recent research emphasizes obtaining meaningful and powerful representations through mining low and high feature interactions using various components such as Deep Neural Networks (DNN), CrossNets, or transformer blocks. In this work, we propose the Deep Multi-Representation model (DeepMR) that jointly trains a mixture of two powerful feature representation learning components, namely DNNs and multi-head self-attentions. Furthermore, DeepMR integrates the novel residual with zero initialization (ReZero) connections to the DNN and the multi-head self-attention components for learning superior input representations. Experiments on three real-world datasets show that the proposed model significantly outperforms all state-of-the-art models in the task of click-through rate prediction.
