基于对比学习的多模态序列推荐算法

针对如何利用商品的多模态信息提高序列推荐算法准确性的问题，提出一种基于对比学习技术的多模态序列推荐算法。该算法首先通过改变商品颜色和截取商品图片中心区域等手段进行数据增强，并把增强后的数据与原数据进行对比学习，以提取到商品的颜色和形状等视觉模态信息；其次对商品的文本模态信息进行低维空间嵌入，从而得到商品多模态信息的完整表达；最后根据商品的时序性，采用循环神经网络（RNN）建模多模态信息的序列交互特征，得到用户的偏好表达，从而进行商品推荐。在两个公开的数据集上进行实验测试的结果表明，与现有的序列推荐算法LESSR相比，所提算法排序性能有所提升，且该算法在特征维度值到达50后，推荐性能基本保持不变。

Multimodal sequential recommendation algorithm based on contrastive learning

A multimodal sequential recommendation algorithm based on contrastive learning technology was proposed to improve the accuracy of sequential recommendation algorithm by using multimodal information of commodities. Firstly， to obtain the visual representations such as the color and shape of the product， the visual modal information of the product was extracted by utilizing the contrastive learning framework， where the data enhancement was performed by changing the color and intercepting the center area of the product. Secondly， the textual information of each commodity was embedded into a low-dimensional space， so that the complete multimodal representation of each commodity could be obtained. Finally， a Recurrent Neural Network （RNN） was used for modeling the sequential interactions of multimodal information according to the time sequence of the product， then the preference representation of user was obtained and used for commodity recommendation. The proposed algorithm was tested on two public datasets and compared with the existing sequential recommendation algorithm LESSR. Experimental results prove that the ranking performance of the proposed algorithm is improved， and the recommendation performance remains basically unchanged after the feature dimension value reaches 50.