Hybrid recommendation model based on multi-head attention mechanism and cross network fusion

The combination of low and high order features in the recommendation system is crucial to the predicted click through rate. This paper designs an Attention Deep Cross Attention Recognition Machine (ADCAFM). Traditional recommendation models only use attention factor decomposers and deep cross networks to extract low and high order features, but the diversity of deep cross networks mining user interests is weak. Therefore, this paper extracts the feature depth of different subspaces by integrating the multi head attention mechanism to solve the problem of user interest diversity in deep cross network mining; Finally, the low and high order combined features are effectively fused and recommended together. Through experimental comparison on Criteo and Movielens-100K data sets, the AUC index is used for evaluation. Compared with the benchmark model, the AUC index is 1.85% and 1.55% higher.