Multi-attribute Context-Aware Item Recommendation Method Based on Deep Learning
Shuxin Li, Lijuan Duan, Wenbo Zhang, Wenjian Wang · 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · 2022
With the wide application of deep learning in the field of recommendation, item recommendation has gradually become a new hotspot. The existing item recommendation methods ignore the context relationship between multiple attributes and fail to make full use of the potential structural interaction relationship. This paper proposes a new multi-attribute context-aware item recommendation method (MCIR) based on deep learning to recommend a series of suitable items for characters in the match. The attribute attention encoder (AAE) module proposed in this method mainly decouples the local and global features of multiple attributes, and the global multiple aggregation (GMA) module integrates the obtained context features in a graph structure. Extensive experiments on the kaggle public game dataset show that our method significantly outperforms previous methods in terms of Precision, F1 and MAP compared to other existing methods.