{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:52:29Z","timestamp":1774453949939,"version":"3.50.1"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Click-through rate (CTR) prediction has become a crucial task in online advertising and other fields. Many researchers focus on improving CTR prediction models by exploring feature interactions. One popular model, Deep Factorization Machine (DeepFM), addresses both high-order and low-order feature interactions, but it overlooks the variability of feature representation in different contexts and lacks a comprehensive explanation of high-order feature interactions. In this paper, we propose a CTR prediction model called DeepFM-GA, which is based on improved feature refinement generation and attention enhancement representation. Firstly, we incorporate an attention convolutional generation module into $\\text{DeepFM}_{\\text{FRNet}}$, which enriches the feature space by generating complementary features through convolutional neural networks while maintaining context-aware feature representation. Secondly, we utilize a multi-head self-attention layer for feature-enhanced representation, enhancing the model\u2019s ability to select important features. Finally, experiments are conducted on four real-world datasets, and the results show that DeepFM-GA has a better performance compared to other mainstream CTR models.<\/jats:p>","DOI":"10.1093\/comjnl\/bxae142","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:02:16Z","timestamp":1736380936000},"page":"697-705","source":"Crossref","is-referenced-by-count":3,"title":["Feature refinement and attention enhancement for click-through rate prediction"],"prefix":"10.1093","volume":"68","author":[{"given":"Sumin","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Engineering , Minzu University of China, 27 Zhongguancun South Avenue, Beijing 100081,","place":["China"]}]},{"given":"Zhen","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Information Engineering , Minzu University of China, 27 Zhongguancun South Avenue, Beijing 100081,","place":["China"]}]},{"given":"Xiuqin","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Information Engineering , Minzu University of China, 27 Zhongguancun South Avenue, Beijing 100081,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,1,9]]},"reference":[{"key":"2025062422042695900_ref1","first-page":"1474354","article-title":"Advertising click-through rate prediction model based on an attention mechanism and a neural network","volume":"2022","author":"Lei","year":"2022","journal-title":"Mobile Inf Syst"},{"key":"2025062422042695900_ref2","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1109\/ICDM.2010.127","article-title":"Factorization machines","volume-title":"Proceedings of the 2010 IEEE International Conference on Data Mining (ICDM)","author":"Rendle","year":"2010"},{"key":"2025062422042695900_ref3","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1145\/2988450.2988454","article-title":"Wide and deep 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