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Inf. Syst."],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>The goal of click-through rate (CTR) prediction in recommender systems is to effectively work with input features. However, existing CTR prediction models face three main issues. First, many models use a basic approach for feature combinations, leading to noise and reduced accuracy. Second, there is no consideration for the varying importance of features in different interaction orders, affecting model performance. Third, current model architectures struggle to capture different interaction signals from various semantic spaces, leading to sub-optimal performance. To address these issues, we propose the Adaptive Graph Interaction Network (AdaGIN) with the Graph Neural Networks-based Feature Interaction Module (GFIM), the Multi-semantic Feature Interaction Module (MFIM), and the Negative Feedback-based Search (NFS) algorithm. GFIM explicitly aggregates information between features and assesses their importance, while MFIM captures information from different semantic spaces. NFS uses negative feedback to optimize model complexity. Experimental results show AdaGIN outperforms existing models on large-scale public benchmark datasets.<\/jats:p>","DOI":"10.1145\/3681785","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T16:11:36Z","timestamp":1721923896000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["AdaGIN: Adaptive Graph Interaction Network for Click-Through Rate Prediction"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1480-6522","authenticated-orcid":false,"given":"Lei","family":"Sang","sequence":"first","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6818-7834","authenticated-orcid":false,"given":"Honghao","family":"Li","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8709-1088","authenticated-orcid":false,"given":"Yiwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8196-0668","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7868-5471","authenticated-orcid":false,"given":"Yun","family":"Yang","sequence":"additional","affiliation":[{"name":"Swinburne University of Technology, Hawthorn, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"12449","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"33","author":"Baevski Alexei","year":"2020","unstructured":"Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli. 2020. wav2vec 2.0: A framework for self-supervised learning of speech representations. 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