{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T05:48:26Z","timestamp":1749793706018,"version":"3.38.0"},"reference-count":40,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2024,2,3]]},"abstract":"<jats:p>Extracting more information from feature interactions is essential to improve click-through rate (CTR) prediction accuracy. Although deep learning technology can help capture high-order feature interactions, the combination of features lacks interpretability. In this paper, we propose a multi-semantic feature interaction learning network (MeFiNet), which utilizes convolution operations to map feature interactions to multi-semantic spaces to improve their expressive ability and uses an improved Squeeze &amp; Excitation method based on SENet to learn the importance of these interactions in different semantic spaces. The Squeeze operation helps to obtain the global importance distribution of semantic spaces, and the Excitation operation helps to dynamically re-assign the weights of semantic features so that both semantic diversity and feature diversity are considered in the model. The generated multi-semantic feature interactions are concatenated with the original feature embeddings and input into a deep learning network. Experiments on three public datasets demonstrate the effectiveness of the proposed model. Compared with state-of-the-art methods, the model achieves excellent performance (+0.18% in AUC and -0.34% in LogLoss VS DeepFM; +0.19% in AUC and -0.33% in LogLoss VS FiBiNet).<\/jats:p>","DOI":"10.3233\/ida-227113","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T16:31:22Z","timestamp":1702398682000},"page":"261-278","source":"Crossref","is-referenced-by-count":1,"title":["MeFiNet: Modeling multi-semantic convolution-based feature interactions for CTR prediction"],"prefix":"10.1177","volume":"28","author":[{"given":"Cairong","family":"Yan","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Donghua University, Shanghai, China"},{"name":"Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai, China"}]},{"given":"Xiaoke","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Donghua University, Shanghai, China"}]},{"given":"Ran","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Donghua University, Shanghai, China"}]},{"given":"Zhaohui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Donghua University, Shanghai, China"},{"name":"Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai, China"}]},{"given":"Yongquan","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Information Technology, Shanghai Jian Qiao University, Shanghai, China"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-227113_ref1","doi-asserted-by":"crossref","unstructured":"W. Ouyang, X. Zhang, S. Ren, C. Qi, Z. Liu and Y. Du, Representation learning-assisted click-through rate prediction, in: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019, pp. 4561\u20134567.","DOI":"10.24963\/ijcai.2019\/634"},{"key":"10.3233\/IDA-227113_ref2","doi-asserted-by":"crossref","unstructured":"X. He, J. Pan, O. Jin, T. Xu, B. Liu, T. Xu, Y. Shi, A. Atallah, R. Herbrich, S. Bowers et al., Practical lessons from predicting clicks on ads at facebook, in: Proceedings of the 8th International Workshop on Data Mining for Online Advertising (ADKDD), 2014, pp.\u00a01\u20139.","DOI":"10.1145\/2648584.2648589"},{"issue":"4","key":"10.3233\/IDA-227113_ref3","doi-asserted-by":"crossref","first-page":"4701","DOI":"10.1007\/s10489-021-02678-8","article-title":"JointCTR: A joint CTR prediction framework combining feature interaction and sequential behavior learning","volume":"52","author":"Yan","year":"2022","journal-title":"Applied Intelligence"},{"key":"10.3233\/IDA-227113_ref4","doi-asserted-by":"crossref","unstructured":"Y. Juan, Y. Zhuang, W.-S. Chin and C.-J. Lin, Field-aware factorization machines for CTR prediction, in: Proceedings of the 10th ACM Conference on Recommender Systems (RecSys), 2016, pp.\u00a043\u201350.","DOI":"10.1145\/2959100.2959134"},{"issue":"6","key":"10.3233\/IDA-227113_ref5","doi-asserted-by":"crossref","first-page":"3189","DOI":"10.1007\/s10489-020-01951-6","article-title":"Modeling low-and high-order feature interactions with FM and self-attention network","volume":"51","author":"Yan","year":"2021","journal-title":"Applied Intelligence"},{"key":"10.3233\/IDA-227113_ref6","doi-asserted-by":"crossref","unstructured":"R. Wang, B. Fu, G. Fu and M. Wang, Deep & cross network for ad click predictions, in: Proceedings of the AdKDD, 2017, pp.\u00a01\u20137.","DOI":"10.1145\/3124749.3124754"},{"key":"10.3233\/IDA-227113_ref7","doi-asserted-by":"crossref","unstructured":"W. Zhang, S. Yuan and J. Wang, Optimal real-time bidding for display advertising, in: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2014, pp.\u00a01077\u20131086.","DOI":"10.1145\/2623330.2623633"},{"key":"10.3233\/IDA-227113_ref8","doi-asserted-by":"crossref","unstructured":"X. Yang, T. Deng, W. Tan, X. Tao, J. Zhang, S. Qin and Z. Ding, Learning compositional, visual and relational representations for CTR prediction in sponsored search, in: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), 2019, pp.\u00a02851\u20132859.","DOI":"10.1145\/3357384.3357833"},{"key":"10.3233\/IDA-227113_ref9","doi-asserted-by":"crossref","unstructured":"W. Zhang, J. Qin, W. Guo, R. Tang and X. He, Deep Learning for Click-Through Rate Estimation, in: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021, pp. 4695\u20134703.","DOI":"10.24963\/ijcai.2021\/636"},{"issue":"3","key":"10.3233\/IDA-227113_ref10","first-page":"57","article-title":"Factorization machines with libfm","volume":"3","author":"Rendle","year":"2012","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"10.3233\/IDA-227113_ref11","doi-asserted-by":"crossref","unstructured":"Y. Shan, T.R. Hoens, J. Jiao, H. Wang, D. Yu and J. Mao, Deep crossing: Web-scale modeling without manually crafted combinatorial features, in: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2016, pp.\u00a0255\u2013262.","DOI":"10.1145\/2939672.2939704"},{"key":"10.3233\/IDA-227113_ref12","doi-asserted-by":"crossref","unstructured":"X. He and T.-S. Chua, Neural factorization machines for sparse predictive analytics, in: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2017, pp.\u00a0355\u2013364.","DOI":"10.1145\/3077136.3080777"},{"key":"10.3233\/IDA-227113_ref13","doi-asserted-by":"crossref","unstructured":"R. Wang, R. Shivanna, D. Cheng, S. Jain, D. Lin, L. Hong and E. Chi, DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems, in: Proceedings of the World Wide Web Conference (WWW), 2021, pp.\u00a01785\u20131797.","DOI":"10.1145\/3442381.3450078"},{"key":"10.3233\/IDA-227113_ref14","doi-asserted-by":"crossref","unstructured":"Y. Qu, H. Cai, K. Ren, W. Zhang, Y. Yu, Y. Wen and J. Wang, Product-based neural networks for user response prediction, in: Proceedings of the 16th IEEE International Conference on Data Mining (ICDM), 2016, pp.\u00a01149\u20131154.","DOI":"10.1109\/ICDM.2016.0151"},{"key":"10.3233\/IDA-227113_ref15","doi-asserted-by":"crossref","unstructured":"Q. Liu, F. Yu, S. Wu and L. Wang, A convolutional click prediction model, in: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM), 2015, pp.\u00a01743\u20131746.","DOI":"10.1145\/2806416.2806603"},{"key":"10.3233\/IDA-227113_ref16","doi-asserted-by":"crossref","unstructured":"B. Liu, R. Tang, Y. Chen, J. Yu, H. Guo and Y. Zhang, Feature generation by convolutional neural network for click-through rate prediction, in: Proceedings of the World Wide Web Conference (WWW), 2019, pp.\u00a01119\u20131129.","DOI":"10.1145\/3308558.3313497"},{"issue":"8","key":"10.3233\/IDA-227113_ref17","doi-asserted-by":"crossref","first-page":"2000","DOI":"10.1109\/TKDE.2016.2562621","article-title":"Contextual operation for recommender systems","volume":"28","author":"Wu","year":"2016","journal-title":"IEEE Transactions on Knowledge and Data Engineering (TKDE)"},{"key":"10.3233\/IDA-227113_ref18","doi-asserted-by":"crossref","unstructured":"Z. Li, Z. Cui, S. Wu, X. Zhang and L. Wang, Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction, in: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), 2019, pp.\u00a0539\u2013548.","DOI":"10.1145\/3357384.3357951"},{"key":"10.3233\/IDA-227113_ref19","doi-asserted-by":"crossref","unstructured":"M. Richardson, E. Dominowska and R. Ragno, Predicting clicks: estimating the click-through rate for new ads, in: Proceedings of the 16th International Conference on World Wide Web (WWW), 2007, pp.\u00a0521\u2013530.","DOI":"10.1145\/1242572.1242643"},{"key":"10.3233\/IDA-227113_ref20","doi-asserted-by":"crossref","unstructured":"C. Yan, Q. Zhang, X. Zhao and Y. Huang, An intelligent field-aware factorization machine model, in: Proceedings of the International Conference on Database Systems for Advanced Applications (DASFAA), 2017, pp.\u00a0309\u2013323.","DOI":"10.1007\/978-3-319-55753-3_20"},{"key":"10.3233\/IDA-227113_ref21","doi-asserted-by":"crossref","unstructured":"H. Guo, R. Tang, Y. Ye, Z. Li and X. He, DeepFM: a factorization-machine based neural network for CTR prediction, in: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017, pp. 1725\u20131731.","DOI":"10.24963\/ijcai.2017\/239"},{"key":"10.3233\/IDA-227113_ref22","doi-asserted-by":"crossref","unstructured":"W. Zhang, T. Du and J. Wang, Deep learning over multi-field categorical data, in: Proceedings of the European Conference on Information Retrieval (ECIR), 2016, pp.\u00a045\u201357.","DOI":"10.1007\/978-3-319-30671-1_4"},{"key":"10.3233\/IDA-227113_ref23","doi-asserted-by":"crossref","unstructured":"H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir et al., Wide & deep learning for recommender systems, in: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (RecSys), 2016, pp.\u00a07\u201310.","DOI":"10.1145\/2988450.2988454"},{"key":"10.3233\/IDA-227113_ref24","doi-asserted-by":"crossref","unstructured":"J. Xiao, H. Ye, X. He, H. Zhang, F. Wu and T.-S. Chua, Attentional factorization machines: Learning the weight of feature interactions via attention networks, in: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017, pp. 3119\u20133312.","DOI":"10.24963\/ijcai.2017\/435"},{"key":"10.3233\/IDA-227113_ref25","doi-asserted-by":"crossref","unstructured":"G. Zhou, X. Zhu, C. Song, Y. Fan, H. Zhu, X. Ma, Y. Yan, J. Jin, H. Li and K. Gai, Deep interest network for click-through rate prediction, in: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (SIGKDD), 2018, pp.\u00a01059\u20131068.","DOI":"10.1145\/3219819.3219823"},{"key":"10.3233\/IDA-227113_ref26","doi-asserted-by":"crossref","unstructured":"W. Xu, H. He, M. Tan, Y. Li, J. Lang and D. Guo, Deep interest with hierarchical attention network for click-through rate prediction, in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020, pp.\u00a01905\u20131908.","DOI":"10.1145\/3397271.3401310"},{"key":"10.3233\/IDA-227113_ref27","doi-asserted-by":"crossref","unstructured":"W. Song, C. Shi, Z. Xiao, Z. Duan, Y. Xu, M. Zhang and J. Tang, Autoint: Automatic feature interaction learning via self-attentive neural networks, in: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), 2019, pp.\u00a01161\u20131170.","DOI":"10.1145\/3357384.3357925"},{"key":"10.3233\/IDA-227113_ref28","doi-asserted-by":"crossref","unstructured":"J. Hu, L. Shen and G. Sun, Squeeze-and-excitation networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp.\u00a07132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"10.3233\/IDA-227113_ref29","doi-asserted-by":"crossref","unstructured":"T. Huang, Z. Zhang and J. Zhang, FiBiNET: Combining feature importance and bilinear feature interaction for click-through rate prediction, in: Proceedings of the 13th ACM Conference on Recommender Systems (RecSys), 2019, pp.\u00a0169\u2013177.","DOI":"10.1145\/3298689.3347043"},{"key":"10.3233\/IDA-227113_ref30","doi-asserted-by":"crossref","unstructured":"Y. Pang, Y. Li, J. Shen and L. Shao, Towards bridging semantic gap to improve semantic segmentation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp.\u00a04230\u20134239.","DOI":"10.1109\/ICCV.2019.00433"},{"key":"10.3233\/IDA-227113_ref31","doi-asserted-by":"crossref","unstructured":"J. Xu, X. He and H. Li, Deep learning for matching in search and recommendation, in: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018, pp.\u00a01365\u20131368.","DOI":"10.1145\/3209978.3210181"},{"issue":"8","key":"10.3233\/IDA-227113_ref33","doi-asserted-by":"crossref","first-page":"3549","DOI":"10.1109\/TKDE.2020.3028705","article-title":"A survey on knowledge graph-based recommender systems","volume":"34","author":"Guo","year":"2020","journal-title":"EEE Transactions on Knowledge and Data Engineering (TKDE)"},{"key":"10.3233\/IDA-227113_ref34","doi-asserted-by":"crossref","unstructured":"X. Chen, X. Yuan, S. Yan, J. Tang, Y. Rui and T.-S. Chua, Towards multi-semantic image annotation with graph regularized exclusive group lasso, in: Proceedings of the 19th ACM International Conference on Multimedia (MM), 2011, pp.\u00a0263\u2013272.","DOI":"10.1145\/2072298.2072334"},{"key":"10.3233\/IDA-227113_ref35","doi-asserted-by":"crossref","unstructured":"S. Wu, F. Yu, X. Yu, Q. Liu, L. Wang, T. Tan, J. Shao and F. Huang, TFNet: Multi-semantic feature interaction for CTR prediction, in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020, pp.\u00a01885\u20131888.","DOI":"10.1145\/3397271.3401304"},{"key":"10.3233\/IDA-227113_ref36","unstructured":"D. Bahdanau, K. Cho and Y. Bengio, Neural machine translation by jointly learning to align and translate, in: Proceedings of the 3rd International Conference on Learning Representations (ICLR), 2015, pp.\u00a01\u201315."},{"key":"10.3233\/IDA-227113_ref37","doi-asserted-by":"crossref","unstructured":"M.-T. Luong, H. Pham and C.D. Manning, Effective approaches to attention-based neural machine translation, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015, pp.\u00a01412\u20131421.","DOI":"10.18653\/v1\/D15-1166"},{"key":"10.3233\/IDA-227113_ref38","doi-asserted-by":"crossref","unstructured":"J. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho and Y. Bengio, Attention-based models for speech recognition, in: Proceedings of Advances in Neural Information Processing Systems (NIPS), 2015, pp.\u00a0577\u2013585.","DOI":"10.1109\/ICASSP.2016.7472618"},{"key":"10.3233\/IDA-227113_ref39","doi-asserted-by":"crossref","unstructured":"D. Bahdanau, J. Chorowski, D. Serdyuk, P. Brakel and Y. Bengio, End-to-end attention-based large vocabulary speech recognition, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp.\u00a04945\u20134949.","DOI":"10.1109\/ICASSP.2016.7472618"},{"key":"10.3233\/IDA-227113_ref40","doi-asserted-by":"crossref","unstructured":"W. Guo, R. Tang, H. Guo, J. Han, W. Yang and Y. Zhang, Order-aware embedding neural network for CTR prediction, in: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2019, pp.\u00a01121\u20131124.","DOI":"10.1145\/3331184.3331332"},{"issue":"1","key":"10.3233\/IDA-227113_ref41","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.neunet.2019.09.020","article-title":"Operation-aware neural networks for user response prediction","volume":"121","author":"Yang","year":"2020","journal-title":"Neural Networks"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-227113","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T05:27:37Z","timestamp":1741670857000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-227113"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,3]]},"references-count":40,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/ida-227113","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"type":"print","value":"1088-467X"},{"type":"electronic","value":"1571-4128"}],"subject":[],"published":{"date-parts":[[2024,2,3]]}}}