An Image Recommendation Algorithm Based on Target Alternating Attention and User Affiliation Network

Abstract

Currently, how to exploit the deep features of images in image recommender systems to achieve image enhancement still needs further research. In addition, little research has explored the implicit and increasing preferences of users by using the affiliation generated by indirect users and virtual users of the main users, which leads to the phenomenon of information cocoon. An Image Recommendation Algorithm Based on Target Alternating Attention and User Affiliation Network (TAUA) is proposed in this paper that addresses the problems of inadequate extraction of semantic features in an image and information cocoon in image recommender systems. First, to complete the multi-dimensional description of the image, we extract the category, color, and style features of the image through a multi-channel convolutional neural network (MCNN), and we then perform migration and integration on these features. Then, to enhance the pixel-level representation ability of the image and achieve image feature enhancement, we propose target alternating attention to capture the information of surrounding pixels alternately from inside to outside. Finally, a user affiliation network, including indirect users and virtual users, is established according to the user behavior and transaction record, and the users’ increasing preferences and affiliated users are mined through the implicit interaction relationship of users. Experimental results show that compared with baselines on the Amazon dataset, the results of F@10, NDCG@10, and AUC of the proposed algorithm are 4.02%, 5.00%, and 2.14% higher than those of ACF, and 5.76%, 0.86% and 1.16% higher than those of VPOI. On the Flickr dataset, our algorithm outperforms ACF by 5.74%, 5.12%, and 3.68% in F@10, NDCG@10, and AUC, respectively, and outperforms VPOI by 0.45%, 0.47%, and 0.49%. TAUA has better recommendation performance and can significantly improve the recommendation effect.