LaViC: Adapting Large Vision-Language Models to Visually-Aware Conversational Recommendation
Hyunsik Jeon, Satoshi Koide, Yu Wang, Zhankui He, Julian McAuley·March 30, 2025
Summary
LaViC integrates visual data into dialogue-based recommendation systems, using a two-stage process for visual knowledge self-distillation and recommendation prompt tuning. It outperforms text-only methods and vision-language baselines, emphasizing visual data's role in capturing product attributes. LaViC highlights visual information's importance in visually oriented domains, enhancing recommendation quality. It references 35 works on conversational recommendation systems, including advancements in conversational AI, multimodal interactions, and user preference modeling. Contributions include Sentence-BERT, LLaMA, Llama 2, DualGNN, and LLaVA-v1.6, addressing conversational recommendation, multimedia recommendation, and large language models.
Introduction
Background
Overview of dialogue-based recommendation systems
Importance of visual data in product recommendation
Objective
To integrate visual data into dialogue-based recommendation systems using a two-stage process for visual knowledge self-distillation and recommendation prompt tuning
Method
Two-Stage Process
Stage 1: Visual Knowledge Self-Distillation
Techniques for extracting visual knowledge from images
Methods for distilling visual information into a form usable by recommendation systems
Stage 2: Recommendation Prompt Tuning
Strategies for refining recommendation prompts with visual inputs
Integration of visual data to enhance the relevance and personalization of recommendations
Performance Evaluation
Comparison with text-only methods
Benchmarking against vision-language baselines
Visual Data's Role
Capturing Product Attributes
How visual data contributes to a deeper understanding of product characteristics
Enhancing Recommendation Quality
The impact of visual information on improving the accuracy and relevance of recommendations
Contributions and References
Relevant Works
35 works on conversational recommendation systems
Advances in conversational AI, multimodal interactions, and user preference modeling
Key Technologies
Sentence-BERT, LLaMA, Llama 2, DualGNN, and LLaVA-v1.6
Contributions to conversational recommendation, multimedia recommendation, and large language models
Conclusion
Summary of LaViC's Achievements
Future Directions
Potential improvements and extensions of LaViC's approach
Ongoing research challenges in integrating visual data into dialogue-based recommendation systems
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What innovative approaches does LaViC employ to enhance recommendation quality in visually oriented domains?
How does LaViC compare to text-only methods and vision-language baselines in terms of recommendation quality?
How does LaViC integrate visual data into dialogue-based recommendation systems?
What are the key contributions of LaViC in the context of conversational recommendation systems?