Research On Click-through-rate Prediction Model Based On Deep Learning

With the advancement of mobile internet and cloud computing,various online advertisements and news proliferate.As a result,it becomes difficult for users to directly filter out target information from a large amount of complex and high-dimensional data.To resolve this problem,many deep learning based recommendation system techniques are proposed and have made breakthrough achievements in practical applications.However,in the advertisement recommendation task,data features are usually multi-domain,multi-type,and less related.Although existing mainstream models(e.g.,x Deep FM),combine explicitly or implicitly features to learn more information,there are still some important issues:(1)Implicit high-order feature interactions usually use feed-forward neural networks to implement feature interaction,but the feature representation of such non-linear combination is insufficient and thus leads to limited performance;(2)Existing models generally realize2-order explicit features combination,and thus the high-order implicit feature combination in the integrated model cannot be determined.As a result,the impact of different order feature combinations on the click-through rate prediction is unclear.To resolve the problem of insufficient feature combination,this paper proposes an adaptive extreme deep factorization machine model(Ax Deep FM)by adding a logarithmic transformation layer in front of the feed forward neural network module in the x Deep FM model.The proposed model is able to learn more patterns of feature representation from different orders' feature combinations.We also empirically demonstrate the effectiveness of the proposed method.To be specific,Ax Deep FM achieves 0.8301,0.7872 and 0.7821 in terms of AUC on Movielens20 M,Avazu and Criteo datasets,respectively.To study the influence of different order feature combinations on click-through rate prediction,we additional propose Attention Factorization Machine and Compression Interaction Network Model(AFM&CIN).Specifically,we replace feed-forward neural network in the x Deep FM model with a second-order feature combination module--AFM module,and transform the implicit feature combination and the explicit feature combination model into a pure explicit higher-order feature interaction model.The two modules in theproposed model explicitly interact features,and the compression interaction network layers help to achieve certain-order explicit features combination.Empirically,AFM&CIN achives0.8241,0.7858 and 0.7887 in terms of AUC on Movielens20 M,Avazu and Criteo datasets,which verifies its effectiveness in real-world datasets.In addition,we further explore the impact of different levels of feature combinations on click-through rate prediction by seting the number of feature interaction layers of the AFM&CIN model to 11,15,19,and 23 respectively.