Multimodal Fashion Recommendation System: Intelligent Recommendations Combining CLIP Visual Encoding and Large Model Explanation Generation

The Multimodal Fashion Recommender project introduced in this article integrates CLIP visual encoding, Sentence-Transformer text encoding, session-aware sequence modeling, and large language model explanation generation. It addresses the cold start, semantic gap, and lack of interpretability issues in traditional recommendation systems, providing users with personalized and understandable fashion recommendations.

In the e-commerce and fashion retail sectors, traditional recommendation systems often only provide results without explaining the reasons. Their main pain points include:

• Cold start (lack of data for new products/users)
• Semantic gap (inability to understand the semantic attributes of products)
• Lack of interpretability (users find it hard to trust the recommendation logic)

The system adopts a dual-tower architecture: the user tower encodes preferences and historical behaviors, while the product tower encodes visual (CLIP-extracted image features), text (Sentence-Transformer-processed product descriptions/user queries, etc.), and session sequence (capturing short-term intentions and long-term preferences) information. The LLM inference layer generates natural language explanations, such as explaining recommendation reasons based on users' browsing history.

Multimodal fusion uses a hybrid strategy (early fusion in the product tower, late fusion in final recommendation) and an attention mechanism (dynamically adjusting weights of each modality). For training, contrastive loss/BPR loss are used, combined with random/hard negative sampling, and the model is optimized through multi-task learning (click-through rate, conversion rate, explanation quality).

The system can be applied in:

• Personalized homepages (product streams with explanations)
• Matching recommendations (explaining matching logic)
• Style discovery (expanding users' choices)
• Intelligent customer service (combining recommendations with explanations) These applications enhance user experience and conversion rates.

Key technical challenges and solutions:

• To meet real-time requirements: precompute product embeddings and use ANN search
• Data sparsity: addressed via CLIP's zero-shot capability and user profile cold start
• Explanation quality: resolved through conditional generation, human feedback fine-tuning, and automatic evaluation monitoring

Future development plans include:

• Expanding to video content understanding
• Integrating social signals
• Incorporating AR/VR virtual try-on
• Adding sustainable fashion recommendation dimensions These will further enhance the system's capabilities.

This project demonstrates the innovative application of multimodal and LLM technologies in recommendation systems, improving recommendation accuracy and user trust. In the future, interpretable personalized assistants will become an important direction for e-commerce recommendations.