{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T06:11:55Z","timestamp":1760854315498,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018YFC1503204","61972360","62273290"],"award-info":[{"award-number":["2018YFC1503204","61972360","62273290"]}]},{"name":"National Natural Science Foundation of China","award":["2018YFC1503204","61972360","62273290"],"award-info":[{"award-number":["2018YFC1503204","61972360","62273290"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Click-through rate (CTR) prediction is crucial for computing advertisement and recommender systems. The key challenge of CTR prediction is to accurately capture user interests and deliver suitable advertisements to the right people. However, there are an immense number of features in CTR prediction datasets, which hardly fit when only using an individual feature. To solve this problem, feature interaction that combines several features via an operation is introduced to enhance prediction performance. Many factorizations machine-based models and deep learning methods have been proposed to capture feature interaction for CTR prediction. They follow an enumeration-filter pattern that could not determine the appropriate order of feature interaction and useful feature interaction. The attention logarithmic network (ALN) is presented in this paper, which uses logarithmic neural networks (LNN) to model feature interactions, and the squeeze excitation (SE) mechanism to adaptively model the importance of higher-order feature interactions. At first, the embedding vector of the input was absolutized and a very small positive number was added to the zeros of the embedding vector, which made the LNN input positive. Then, the adaptive-order feature interactions were learned by logarithmic transformation and exponential transformation in the LNN. Finally, SE was applied to model the importance of high-order feature interactions adaptively for enhancing CTR performance. Based on this, the attention logarithmic interaction network (ALIN) was proposed for the effectiveness and accuracy of CTR, which integrated Newton\u2019s identity into ALN. ALIN supplements the loss of information, which is caused by the operation becoming positive and by adding a small positive value to the embedding vector. Experiments are conducted on two datasets, and the results prove that ALIN is efficient and effective.<\/jats:p>","DOI":"10.3390\/e24121831","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T01:46:51Z","timestamp":1671155211000},"page":"1831","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Dual Adaptive Interaction Click-Through Rate Prediction Based on Attention Logarithmic Interaction Network"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6823-735X","authenticated-orcid":false,"given":"Shiqi","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5503-1336","authenticated-orcid":false,"given":"Zhendong","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongquan","family":"Pei","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.elerap.2010.04.005","article-title":"Online advertisement service pricing and an option contract","volume":"10","author":"Moon","year":"2011","journal-title":"Electron. Commer. Res. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pan, J., Xu, J., Ruiz, A.L., Zhao, W., Pan, S., Sun, Y., and Lu, Q. (2018, January 23\u201327). Field-weighted factorization machines for click-through rate prediction in display advertising. Proceedings of the 2018 World Wide Web Conference, Lyon, France.","DOI":"10.1145\/3178876.3186040"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lu, W., Yu, Y., Chang, Y., Wang, Z., Li, C., and Yuan, B. (2021, January 7\u201315). A dual input-aware factorization machine for CTR prediction. Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, Yokohama, Japan.","DOI":"10.24963\/ijcai.2020\/434"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102142","DOI":"10.1016\/j.ipm.2019.102142","article-title":"Graph neural news recommendation with long-term and short-term interest modeling","volume":"57","author":"Hu","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wu, C., Wu, F., Ge, S., Qi, T., Huang, Y., and Xie, X. (2019, January 3\u20137). Neural news recommendation with multi-head self-attention. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China.","DOI":"10.18653\/v1\/D19-1671"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, H., Wu, F., Liu, Z., and Xie, X. (2020, January 5\u201310). Fine-grained interest matching for neural news recommendation. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.","DOI":"10.18653\/v1\/2020.acl-main.77"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, C., Liu, Z., Wu, M., Xu, Y., Huang, P., Zhao, H., Kang, C., Chen, Q., Li, W., and Lee, D.L. (2019, January 3\u20137). Multi-interest network with dynamic routing for recommendation at Tmall. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China.","DOI":"10.1145\/3357384.3357814"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cen, Y., Zhang, J., Zou, X., Zhou, C., Yang, H., and Tang, J. (2020, January 22\u201327). Controllable multi-interest framework for recommendation. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, San Digeo, CA, USA.","DOI":"10.1145\/3394486.3403344"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106522","DOI":"10.1016\/j.knosys.2020.106522","article-title":"Attentive capsule network for click-through rate and conversion rate prediction in online advertising","volume":"211","author":"Li","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, Z., Cui, Z., Wu, S., Zhang, X.-Y., and Wang, L. (2019, January 3\u20137). Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China.","DOI":"10.1145\/3357384.3357951"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rendle, S. (2010, January 13). Factorization machines. Proceedings of the 2010 IEEE International Conference on Data Mining, Washington, DC, USA.","DOI":"10.1109\/ICDM.2010.127"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Juan, Y., Zhuang, Y., Chin, W.S., and Lin, C.-J. (2016, January 15\u201319). Field-aware factorization machines for CTR prediction. Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA.","DOI":"10.1145\/2959100.2959134"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., and Chua, T.-S. (2017). Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv.","DOI":"10.24963\/ijcai.2017\/435"},{"key":"ref_14","first-page":"3359","article-title":"Higher-order factorization machines","volume":"29","author":"Blondel","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_15","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao HY, M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xu, X., Zhao, M., Shi, P., Ren, R., He, X., Wei, X., and Yang, H. (2022). Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN. Sensors, 22.","DOI":"10.3390\/s22031215"},{"key":"ref_18","first-page":"15","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_19","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cheng, H.T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., and Ispir, M. (2016, January 15). Wide & deep learning for recommender systems. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, New York, NY, USA.","DOI":"10.1145\/2988450.2988454"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Richardson, M., Dominowska, E., and Ragno, R. (2007, January 8\u201312). Predicting clicks: Estimating the click-through rate for new ads. Proceedings of the 16th International Conference on World Wide Web, New York, NY, USA.","DOI":"10.1145\/1242572.1242643"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y., Li, Z., and He, X. (2017). DeepFM: A factorization-machine based neural network for CTR prediction. arXiv.","DOI":"10.24963\/ijcai.2017\/239"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.ins.2022.08.009","article-title":"Click-through rate prediction using transfer learning with fine-tuned parameters","volume":"612","author":"Yang","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lian, J., Zhou, X., Zhang, F., Chen, Z., Xie, X., and Sun, C. (2018, January 19\u201323). xdeepfm: Combining explicit and implicit feature interactions for recommender systems. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, New York, NY, USA.","DOI":"10.1145\/3219819.3220023"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.neucom.2022.01.035","article-title":"Multi-scale and multi-channel neural network for click-through rate prediction","volume":"480","author":"Zhang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.ins.2022.10.091","article-title":"Interpretable click-through rate prediction through distillation of the neural additive factorization model","volume":"617","author":"Jose","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1109\/ICNN.1996.549076","article-title":"A logarithmic neural network architecture for unbounded non-linear function approximation","volume":"Volume 2","author":"Hines","year":"1996","journal-title":"Proceedings of the International Conference on Neural Networks (ICNN\u201996)"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cheng, W., Shen, Y., and Huang, L. (2020, January 7\u201312). Adaptive factorization network: Learning adaptive-order feature interactions. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i04.5768"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1080\/00029890.1992.11995923","article-title":"Newton\u2019s identities","volume":"99","author":"Mead","year":"1992","journal-title":"Am. Math. Mon."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yu, F., Liu, Z., Liu, Q., Zhang, H., Wu, S., and Wang, L. (2020, January 19\u201323). Deep interaction machine: A simple but effective model for high-order feature interactions. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, Virtual Event.","DOI":"10.1145\/3340531.3412077"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, R., Fu, B., Fu, G., and Wang, M. (2017, January 14). Deep & cross network for ad click predictions. Proceedings of the ADKDD\u201917, Halifax, NS, Canada.","DOI":"10.1145\/3124749.3124754"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Qu, Y., Cai, H., Ren, K., Zhang, W., Yu, Y., Wen, Y., and Wang, J. (2016, January 12\u201315). Product-based neural networks for user response prediction. Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain.","DOI":"10.1109\/ICDM.2016.0151"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, X., and Chua, T.S. (2017, January 7\u201311). Neural factorization machines for sparse predictive analytics. Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, Tokyo, Japan.","DOI":"10.1145\/3077136.3080777"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"114861","DOI":"10.1016\/j.compstruct.2021.114861","article-title":"Numerical vibration correlation technique for thin-walled composite beams under compression based on accurate refined finite element","volume":"280","author":"Yang","year":"2022","journal-title":"Compos. Struct."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1017\/S0376892997000088","article-title":"A review of methods for the assessment of prediction errors in conservation presence\/absence models","volume":"24","author":"Fielding","year":"1997","journal-title":"Environ. Conserv."},{"key":"ref_37","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/12\/1831\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:42:06Z","timestamp":1760146926000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/12\/1831"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,15]]},"references-count":37,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["e24121831"],"URL":"https:\/\/doi.org\/10.3390\/e24121831","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2022,12,15]]}}}