This survey comprehensively maps the evolution of cold-start recommendations, from basic content features to advanced LLM applications, covering 220 papers through December 2024.
https://arxiv.org/abs/2501.01945
🔍 Original Problem:
→ Recommender systems struggle with new users and items due to lack of historical interaction data, leading to poor recommendations and user engagement.
→ Traditional methods rely heavily on interaction history, making it difficult to handle cold-start scenarios effectively.
💡 Methods explored in this Paper:
→ The paper categorizes cold-start solutions into four knowledge scopes: content features, graph relations, domain information, and LLM world knowledge.
→ Content features focus on user profiles and item descriptions for initial modeling.
→ Graph relations leverage network structures to infer preferences through connections.
→ Domain information transfers knowledge from data-rich domains to cold-start scenarios.
→ LLM knowledge enhances recommendations through pre-trained understanding of user-item relationships.
🎯 Key Insights:
→ LLMs can serve both as direct recommender systems and knowledge enhancers
→ Multi-modal and cross-domain approaches significantly improve cold-start performance
→ Efficiency and privacy remain key challenges in LLM-based recommendations
📊 Results:
→ First comprehensive survey covering 220 papers through December 2024
→ Defines 9 distinct cold-start scenarios across four categories
→ Provides unified taxonomy for cold-start recommendation research
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