The Multimedia Recommendation System Based on Multimodal Fine-Grained Classification Mining
Yifan Huo, Zheng Fan, Ming Liu, Junhong Zheng, Li-Li He · 2025
With the rapid development of e-Commerce, product recommendation systems play a crucial role in enhancing user experience and increasing the volume of transaction on the platform. However, existing recommendation systems generally fail to fully consider the fine-grained features of products and primarily focus on users' positive preference features while neglecting potential negative preference features. This limitation constrains the accuracy and diversity of recommendation systems. To address this, we propose a novel multimedia recommendation model called ''Temporal Causal Fine-grained Recommendation'' (TCFRec). Specifically, we first perform fine-grained feature extraction and classification of products based on the CLIP model and a multi-level complementary attention mechanism. Subsequently, we leverage a personalized time decay strategy and causal contrastive learning to deeply explore both users' positive and negative preferences. Furthermore, counterfactual reasoning is utilized to identify and eliminate spurious correlations in multimodal features. Finally, by integrating users' fine-grained positive preference classification, negative preference classification, and the influence of social networks, we achieve accurate and diversified personalized recommendations. We conducted extensive experiments to verify the effectiveness and rationality of TCFRec.