CFALR: Collaborative Filtering-Augmented Large Language Model for Personalized Fashion Outfit Recommendation
This work addresses the challenge of personalized outfit recommendation in e-commerce and social media, offering a solution that balances user preferences with aesthetic compatibility in data-sparse scenarios.
CFALR introduces a framework combining collaborative filtering with large language models for personalized fashion outfit recommendation, achieving superior performance over both CF-based and LLM-based methods on Polyvore and IQON benchmarks in personalized fill-in-the-blank and outfit generation tasks.
Personalized outfit recommendation poses a significant challenge in e-commerce and social media platforms, requiring systems that balance user preferences with aesthetic compatibility. Collaborative filtering (CF) provides a traditional solution for this, but it struggles with data-sparse scenarios and complex user-item-outfit relationships. Meanwhile, existing template-based approaches are constrained by rigid pre-designed structures. To bridge these research gaps, we introduce CFALR (Collaborative Filtering-Augmented Large Language Model for Recommendation), a novel framework that synergizes collaborative filtering with large language models for personalized outfit recommendation. Specifically, CFALR describes user-outfit interactions in natural language and leverages LLMs to capture fashion semantics while employing CF-enhanced embeddings to bridge the semantic space and the collaborative interaction spaces. Our technical contributions include: (1) the first LLM-based architecture specifically designed for personalized outfit recommendation, (2) a CF-augmented generative mechanism that efficiently navigates the extensive combination space of outfit items, and (3) trainable projection layers that optimally integrate relational and content features. Experiments on Polyvore and IQON benchmarks demonstrate CFALR's superior performance over both traditional CF-based and LLM-based methods in personalized fill-in-the-blank and personalized outfit generation tasks.