[Objective] This paper proposes a deep learning recommendation model with item audience features. It captures collaborative information and the high-order features from users and items the interactions. [Methods] First,we used the attention mechanism to analyze the historical interaction information between items and users. Then, the system adaptively constructed personalized audience features of items. Third, we introduced these features to the model as important supplementary information for preference predictions. We also developed an explicit feature crossing and introduced residual connections to enrich the high-order features. [Results] We examined the new model with three public datasets. It improved the Precision, Recall, F1, and NDCG by up to 9.1%, 9.4%, 9.2%, and 12.1% compared with the sub-optimal method (the recommendation length = 10). [Limitations] The performance of our model relies mainly on the historical interaction data volumes. [Conclusions] The proposed model improves the recommendation quality and shows good application potential.
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