Research on Cross-Platform Image Recommendation Model Fusing Text Information

Abstract

The user data from different types of network platforms are often presented in different modalities, such as text, image, or audio. Many researches have shown that fusing the data information displayed by users on different platforms can better reflect the interest characteristics of users. Hence, this paper proposes a cross-platform image recommendation model (FITIFCIR), which fuses text and image information to achieve cross-platform data recommendation. Furthermore, it realizes the semantic information fusion of text and image, so as to recommend the images collected by users on the image sharing platform to the text to be published. Compared with baseline image recommendation models, the experimental results indicate that the FITIFCIR outperforms baseline models. The proposed model is effective to recommend appropriate images for users to better illustrate their ideas.