Multimodal Recommendation System

• Developed a multimodal deep learning solution for CTR prediction.
• Experimented with different model architectures, including Transformer-based approaches.
• Evaluated and compared model performance using offline validation.
• Optimized the recommendation model based on experimental results.

Fields

Deep Learning · Multimodal Learning · Recommendation Systems

Technologies

Python · Transformers · Multimodal Learning

This project focused on click-through-rate (CTR) prediction within the context of the WWW 2025 MM-CTR Challenge.

I developed and optimized a multimodal recommendation system using deep learning techniques, including Transformer-based architectures.

The work involved experimenting with different model architectures, evaluating their predictive performance, and refining the approach through offline validation.

The project explored how multimodal information can be incorporated into recommendation models to predict user interactions more effectively.