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.
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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.
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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
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DOI: https://doi.org/10.1007/s00500-022-07149-x