关键词: 电子商务, 深度学习, 点击率预测, 多头自注意力机制, 多层感知机

Abstract:

Currently， the most common way to predict Click-Through Rate （CTR） is to extract interest features through feature interaction techniques， but most of these methods ignore the inherent relationships between users and items， and fail to fully explore the potential interests of users implied among items. To address this problem， a Personalized Multi-layer Interest extraction Click-through rate prediction model （PMIC） was proposed， aiming to mine multi-layer interests shown by users at the same time from different perspectives. Firstly， the recall matching method was employed to learn and model the relationships between users and items from both the item learning module and the user learning module， thereby capturing diverse interests of users. Secondly， the multi-head self-attention mechanism was utilized within the item learning module to concurrently extract multiple potential interests. Finally， a corresponding inner product method was adopted to further refine and enhance the feature representation between users and items. Experimental results on multiple public datasets demonstrate that PMIC improves the Area Under the receiver operating characteristic Curve （AUC） by at least 2.3%.

Key words: e-commerce, deep learning, Click-Through Rate (CTR) prediction, multi-head self-attention mechanism, Multi-Layer Perceptron (MLP)