ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding

• URL: http://arxiv.org/abs/2107.12025v1
• Date: Mon, 26 Jul 2021 08:29:40 GMT
• Title: ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding
• Authors: Zhiqiang Wang, Qingyun She, PengTao Zhang, Junlin Zhang
• Abstract summary: We propose a novel CTR Framework named ContextNet that implicitly models high-order feature interactions. We conduct extensive experiments on four real-world datasets and the experiment results demonstrate that our proposed ContextNet-PFFN and ContextNet-SFFN model outperform state-of-the-art models such as DeepFM and xDeepFM significantly.
• Score: 2.146541845019669
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Click-through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems and it's important for ranking models to effectively capture complex high-order features.Inspired by the success of ELMO and Bert in NLP field, which dynamically refine word embedding according to the context sentence information where the word appears, we think it's also important to dynamically refine each feature's embedding layer by layer according to the context information contained in input instance in CTR estimation tasks. We can effectively capture the useful feature interactions for each feature in this way. In this paper, We propose a novel CTR Framework named ContextNet that implicitly models high-order feature interactions by dynamically refining each feature's embedding according to the input context. Specifically, ContextNet consists of two key components: contextual embedding module and ContextNet block. Contextual embedding module aggregates contextual information for each feature from input instance and ContextNet block maintains each feature's embedding layer by layer and dynamically refines its representation by merging contextual high-order interaction information into feature embedding. To make the framework specific, we also propose two models(ContextNet-PFFN and ContextNet-SFFN) under this framework by introducing linear contextual embedding network and two non-linear mapping sub-network in ContextNet block. We conduct extensive experiments on four real-world datasets and the experiment results demonstrate that our proposed ContextNet-PFFN and ContextNet-SFFN model outperform state-of-the-art models such as DeepFM and xDeepFM significantly.

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