Aiming at the problems of data sparsity, uneven behavior weight allocation, and insufficient timeliness modeling existing in traditional recommendation systems in the scenario of personalized fashion recommendation, this paper proposes a personalized recommendation method that integrates multi-behavior weights and multi-modal features. A dynamic weighted collaborative filtering algorithm is designed, which comprehensively considers the multi-dimensional behaviors of users, and introduces a time attenuation factor to construct a time-sensitive user-item scoring matrix, so as to more accurately depict the dynamic changes of user interests. A multi-modal deep fusion framework is built: ResNet-50 is used to extract commodity image features, and the pre-trained BERT model is combined to extract text features; meanwhile, the multi-head self-attention mechanism is adopted to realize semantic-level interaction and adaptive fusion of cross-modal features, thereby enhancing the expressive ability of commodity representation. Then, user preference score prediction is carried out based on the deep predictive network to generate a personalized recommendation list. Experimental results on real e-commerce datasets show that the method in this paper achieves 0.703 and 0.491 on HR@5 and NDCG@5, respectively, which is significantly superior to other baseline models. Ablation experiments further verify the effectiveness of each module including time attenuation, multi-behavior weights and multi-modal features. This study provides a more accurate, dynamic and transparently interpretable personalized recommendation solution for e-commerce platforms, and has certain theoretical value and practical significance.
Lu et al. (Thu,) studied this question.