Memory-powered LLMs now understand your long-term interests, not just recent clicks
Paper proposes AutoMR, a framework that enhances LLM-based recommendations by effectively utilizing users' long-term interaction histories through automated memory retrieval.
https://arxiv.org/abs/2412.17593
Original Problem 💡:
→ LLMs have limited context windows, restricting them to focus only on recent user interactions in recommendation systems.
→ This limitation causes LLMs to miss important long-term user interests and preferences.
Solution in this Paper 🔧:
→ AutoMR stores users' long-term interaction histories in external memory, encoded by LLMs.
→ It uses a trained retriever to extract relevant historical information when needed.
→ The retriever is trained using annotations based on perplexity reduction of ground-truth items.
→ AutoMR combines retrieved long-term data with recent interactions to generate recommendations.
Key Insights 💡:
→ Manual retrieval design is challenging but annotating memory usefulness is straightforward
→ Learning-based retrieval outperforms semantic retrieval for recommendation tasks
→ Distant historical interactions can provide valuable signals for current recommendations
Results 📊:
→ Tested on Amazon Book and Movie datasets from 2017
→ Outperformed baselines: BIGRec, ReLLa, TRSR, and SASRec
→ Achieved 0.0291 Recall@1 and 0.0379 Recall@5 on Book dataset
→ Showed 0.0601 Recall@1 and 0.0638 Recall@5 on Movie dataset
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