## 5 Conclusions and Future Work  
By inputting various attributes and features of users and items, the recommendation system outputs a list of recommendations ranked according to user preferences. User information, item information, and contextual information are the main data sources in recommender systems. However, for image recommendations, image feature extraction and user preference mining are very important, which affect the performance of the recommender system. Although some methods for image feature extraction have achieved certain effects, they still fail to fully excavate deep information, and their effects need to be further improved. The development of deep learning and neural networks brings opportunities to recommender systems, but it requires large-scale data and the calculation cost is relatively high, which limits its application in the recommendation domain to a certain extent. In this study, we apply knowledge transfer and attention to recommender systems. New attention is designed based on the intrinsic relationships between image pixels. Based on this, an image recommendation algorithm based on target alternating attention is proposed for better recommend e-commerce users from the perspective of images. Specifically, a user item joint recommendation method is designed to realize cross-domain recommendations by mining hidden cross-fusion preferences between users and items. Experimental results on the Amazon and Flickr datasets show that the proposed method is effective in image processing and recommendations and successfully improves the ability of image recommendations in the e-commerce field. TAUA, as a deep-learning-based recommendation method, provides positive guidance in the field of image recommendations and can be applied to more fields. However, the multi-dimensional image feature extraction designed in this paper only trains and classifies the categories, colors, and styles of images, which still falls short of the actual needs of e-commerce platforms. In future work, in addition to style features, more useful user features and items can be explored. The number of MCNN channels can be expanded. Target alternating attention collects more accurate pixel contextual information, but the computational complexity still needs to be reduced. In the future, we will try to design a more concise attention mechanism to further reduce the complexity of image recommendation algorithms. Conceptualization, S.W. and D.Q. ; methodology, S.W., S.Y. and Y.L. ; software, D.Q. ; validation, J.D. and C.W. ; formal analysis, Y.L. ; investigation, S.W. ; data curation, S.Y. ; writing--original draft preparation, S.W. and S.Y. ; writing--review and editing, J.D and C.W. ; supervision, C.W. ; project administration, C.W. ; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript. **Funding:** The authors wish to acknowledge the support of the National Natural Science Foundation of China (No. 61902016), the Education and Research Project of Beijing University of Civil Engineering and Architecture (No. Y2009), the Postgraduate Education and Teaching Quality Improvement Project of Beijing University of Civil Engineering and Architecture, China (No. J2023002, J2022005), and BUCEA Post Graduate Innovation Project (No. PG2023086, PG2022080, PG2022082). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data is contained within the article.