Collaborative filtering meets natural language understanding to solve cold-start recommendations
RecLM integrates LLMs with collaborative filtering to enhance recommendation systems, particularly addressing cold-start scenarios through instruction tuning and reinforcement learning.
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https://arxiv.org/abs/2412.19302v2
Original Problem 🤔:
→ Traditional recommendation systems struggle with cold-start scenarios where new items lack interaction history
→ Current systems heavily depend on user-item IDs, limiting their ability to handle new items effectively
→ External features like text descriptions are often noisy or incomplete
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Solution in this Paper 🔧:
→ RecLM introduces a novel instruction-tuning framework that teaches LLMs to understand user-item relationships
→ The system employs a two-turn dialogue approach where LLMs first generate user profiles based on interaction history
→ It then uses reinforcement learning to refine these profiles, ensuring personalization while maintaining collaborative patterns
→ The framework seamlessly integrates with existing recommender systems as a plug-and-play component
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Key Insights 💡:
→ LLMs can effectively bridge the gap between text features and user preferences
→ Two-turn dialogue instruction tuning significantly improves profile generation quality
→ Reinforcement learning helps balance personalization and collaborative filtering
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Results 📊:
→ Achieves 102.57% improvement in Recall@20 on MIND dataset
→ Shows 395.81% enhancement in NDCG scores for zero-shot recommendations
→ Demonstrates consistent performance gains across various recommendation models
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