融合注意力机制的互补产品推荐方法

互补产品推荐是为用户推荐可以一起搭配使用的产品。现有产品推荐方法考虑了产品的图像与文本的所有特征，但没有考虑到视觉和文本模态间的关系，此外并不是所有的特征对互补关系的贡献都相同。基于此种情况，提出了一种融合注意力机制的互补产品推荐模型（complementary product recommendation fusing with attention mechanism，CPRFA）。该模型首先用产品的图像和文本信息来丰富其特征表示；其次为了将异质产品与多模态信息统一起来，将其进一步转换为图像和文本向量表示；然后使用注意力机制自适应地为产品的不同特征分配权重；最后学习产品与候选产品之间的关系，进行互补产品的推荐。在基于Amazon数据集的实验中，所提CPRFA模型在各项指标上均优于其他基线模型，表明CPRFA模型可以更准确地为用户推荐互补产品。

Complementary product recommendation fusing with attention mechanism

Complementary product recommendation is to recommend products to users that can be used together. Existing product recommendation methods consider all features of product''s images and texts, but do not consider the relationship between visual and textual modalities, and not all features contribute equally to the complementary relationship. Based on this situation, this paper proposed a complementary product recommendation model fusing with attention mechanism(CPRFA). The model firstly enriched its feature representation with the product''s image and text information. Secondly, in order to unify heterogeneous products with multimodal information, it further converted them into image and text vector representations, and then used the attention mechanism to adaptively assign weights to different features of the product. Finally, it learned the relationship between the product and the candidate products for the recommendation of complementary products. In the experiments based on the Amazon dataset, the CPRFA model outperforms other baseline models in all indicators, which shows that it can recommend complementary products to users more accurately.