Abstract:  The combination of low-order and high-order features in the recommendation system is crucial to the predicted click-through rate. This paper designs an Attention Deep Cross IO-awre Factorization Machine （ADCIOFM） model. The traditional recommendation model extracts low-order and high-order features through the attention factor decomposer and deep cross-network respectively. However， the hidden field information is easily ignored when the attention factor decomposer extracts low-order combined features， and the diversity of user interests in deep cross-network mining is weak. Therefore， this paper enhances the representation ability of attention mechanism to estimate the low-order combined feature weight by incorporating the perceptual auxiliary matrix. The feature depth of different subspaces is extracted by integrating a multi-head attention mechanism to solve the problem of user interest diversity in deep cross-network mining. Finally， the low-order and high-order combined features are effectively fused for a recommendation. Through experimental comparison on Criteo and Movielens-100K data sets， the AUC index is used for evaluation， which is 0.0087 and 0.0159 higher than the benchmark model.

Key words: click-through rate, perceived attention factor decomposer, cross network, multi-head attention mechanism, deep neural network