Abstract
Advertising Click-Through Rate (CTR) prediction is a relatively successful application in the field of recommendation system. Improving the accuracy of advertising click-through rate can not only make the user experience better, but also give more benefits to advertising platforms and advertisers. It can be seen from the research status that the interaction between learning features has become a very important part of the advertising CTR prediction model. Although the existing CTR prediction models based on deep learning have achieved good results, some models only consider a single interaction mode and lack the diversity of feature interaction. To resolve this problem, this paper proposes a CTR prediction model based on multi-feature interaction, called EDIF, which aims to enhance the diversity of feature interaction. Firstly, the model learns multiple different embedding vectors for each feature in the embedding layer, which reflects the correlation between features; secondly, in the interaction of high-order features, the embedding vectors of each feature are added and pooled to form the aggregation vector of the whole feature as the input, which reflects the integrity of the feature; finally, after the feature embedding operation, the model introduces two layers in parallel: compressed excitation network layer and explicit high-order interaction layer, which improves the ability of feature interaction. We have done a lot of experiments on two public data sets, Avazu and Criteo. The results show that the model effect of this paper has great advantages over the latest model.
Similar content being viewed by others
Data availability
A data availability statement is mandatory for publication in this journal. Please confirm that this statement is accurate, or provide an alternative.
References
Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609
Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-Qaness MA, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250
Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH (2022) Reptile search algorithm (rsa): A nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158
Abualigah LMQ et al (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Cham
Agushaka JO, Ezugwu AE, Abualigah L (2022) Dwarf mongoose optimization algorithm. Comput Methods Appl Mech Eng 391:114570
Cheng HT, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M, et al. (2016) Wide & deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems, pp 7–10
Feng Y, Lv F, Shen W, Wang M, Sun F, Zhu Y, Yang K (2019) Deep session interest network for click-through rate prediction. arXiv preprint arXiv:1905.06482
Guo H, Tang R, Ye Y, Li Z, He X (2017) Deepfm: a factorization-machine based neural network for ctr prediction. arXiv preprint arXiv:1703.04247
He X, Chua TS (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, pp 355–364
Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141
Huang T, Zhang Z, Zhang J (2019) Fibinet: combining feature importance and bilinear feature interaction for click-through rate prediction. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp 169–177
Juan Y, Zhuang Y, Chin WS, Lin CJ (2016) Field-aware factorization machines for ctr prediction. In: Proceedings of the 10th ACM conference on recommender systems, pp 43–50
Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980
Lian J, Zhou X, Zhang F, Chen Z, Xie X, Sun G (2018) xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 1754–1763
Liu B, Zhu C, Li G, Zhang W, Lai J, Tang R, He X, Li Z, Yu Y (2020) Autofis: Automatic feature interaction selection in factorization models for click-through rate prediction. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2636–2645
Lyu Z, Dong Y, Huo C, Ren W (2020) Deep match to rank model for personalized click-through rate prediction. Proc AAAI Conf Artif Intel 34:156–163
Ouyang W, Zhang X, Li L, Zou H, Xing X, Liu Z, Du Y (2019a) Deep spatio-temporal neural networks for click-through rate prediction. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2078–2086
Ouyang W, Zhang X, Ren S, Li L, Liu Z, Du Y (2019b) Click-through rate prediction with the user memory network. In: Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data, pp 1–4
Oyelade ON, Ezugwu AES, Mohamed TI, Abualigah L (2022) Ebola optimization search algorithm: a new nature-inspired metaheuristic optimization algorithm. IEEE Access 10:16150–16177
Pasricha R, McAuley J (2018) Translation-based factorization machines for sequential recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp 63–71
Pi Q, Bian W, Zhou G, Zhu X, Gai K (2019) Practice on long sequential user behavior modeling for click-through rate prediction. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2671–2679
Qu Y, Cai H, Ren K, Zhang W, Yu Y, Wen Y, Wang J (2016) Product-based neural networks for user response prediction. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), IEEE, pp 1149–1154
Rendle S (2010) Factorization machines. In: 2010 IEEE International conference on data mining, IEEE, pp 995–1000
Song W, Shi C, Xiao Z, Duan Z, Xu Y, Zhang M, Tang J (2019) Autoint: Automatic feature interaction learning via self-attentive neural networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp 1161–1170
Wang R, Fu B, Fu G, Wang M (2017) Deep & cross network for ad click predictions. In: Proceedings of the ADKDD’17, pp 1–7
Xiao J, Ye H, He X, Zhang H, Wu F, Chua TS (2017) Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv preprint arXiv:1708.04617
Xu W, He H, Tan M, Li Y, Lang J, Guo D (2020) 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, pp 1905–1908
Yang Y, Xu B, Shen S, Shen F, Zhao J (2020) Operation-aware neural networks for user response prediction. Neural Netw 121:161–168
Zhang W, Du T, Wang J (2016) Deep learning over multi-field categorical data. In: European conference on information retrieval, Springer, pp 45–57
Zhou C, Bai J, Song J, Liu X, Zhao Z, Chen X, Gao J (2018a) Atrank: An attention-based user behavior modeling framework for recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 32
Zhou G, Zhu X, Song C, Fan Y, Zhu H, Ma X, Yan Y, Jin J, Li H, Gai K (2018b) Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 1059–1068
Zhou G, Mou N, Fan Y, Pi Q, Bian W, Zhou C, Zhu X, Gai K (2019) Deep interest evolution network for click-through rate prediction. Proc AAAI Conf Artif Intel 33:5941–5948
Funding
No funding was received for conducting this study.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Leilei Yang, Wenguang Zheng and Yingyuan Xiao. The first draft of the manuscript was written by Leilei Yang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Conflict of interest
The authors declare that they have no conflict of interest.
Informed consent
Informed consent was not required as no humans or animals were involved.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yang, L., Zheng, W. & Xiao, Y. Exploring different interaction among features for CTR prediction. Soft Comput 26, 6233–6243 (2022). https://doi.org/10.1007/s00500-022-07149-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-022-07149-x