Abstract: The hybrid recommendation model,named IEU-DeepCFM (deep and cross factorization machine information extraction unit),is proposed in this paper,which is based on the deep factorization machine and integrates the information extraction unit and cross network structure. In the proposed model,a fixed representation of each feature is learned by most existing recommendation methods. However,it is recognized that user behavioral preferences change with contextual features,and features have different importance in different contexts. Therefore,inaccurate recommendation results may be caused by the fixed representation of features given by the model. To address this issue,the information extraction unit module is introduced,consisting of a self-attention mechanism and a contextual information extractor. This module learns context-aware feature representations for each feature in various contexts. Subsequently,a deep cross factorization machine is employed to mine low- and high-order features of the user. This enables users to receive more explicit cross-information,ultimately leading to click-through rate predictions based on user behavioral characteristics. The results of ablation and comparison experiments conducted on the MovieLens movie dataset and the Avazu advertising click-through rate dataset demonstrate the improvement in both AUC and LogLoss indicators achieved by the proposed model. This confirms the rationality of the model.

Key words: deep learning, contextual features, information extraction unit, recommendation algorithm, self-attention mechanism