Abstract:Explainable recommendation algorithms utilize behavioral and other relevant information to not only generate recommendation results but also provide recommendation explanations, thereby increasing the transparency and credibility of recommendations. Traditional explainable recommendation algorithms are often limited to analyzing rating data and text data and fail to fully utilize data such as images. They also do not consider effective fusion methods between modalities, making it difficult to fully unearth the intrinsic relationships between different modalities. An explainable recommendation model that fuses multimodal features is proposed to address the above-mentioned issues. This model improves the quality and personalization of recommendation explanations from a multimodal perspective through feature fusion technology. Firstly, a multimodal feature extraction method is designed based on CLIP image encoder and text encoder to extract text and image features of users and items, respectively. Secondly, cross attention technology is used to achieve cross modal fusion of text and images, enhancing semantic correlation between modalities. Finally, multimodal information is combined with interactive information to jointly optimize modal alignment, rating prediction, and explanation generation. Experimental results show that the proposed method exhibits significant advantages in the three multimodal recommendation datasets, especially in improving explanation quality.