"Preference Discerning with LLM-Enhanced Generative Retrieval"

Making recommendation systems that listen to user preferences, not just watch their actions.

Sequential recommendation systems struggle with personalization because they can't explicitly understand user preferences.

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https://arxiv.org/abs/2412.08604

🤔 Original Problem:

→ Current recommendation systems rely on implicit modeling of user preferences from interaction history, leading to limited personalization and inability to adapt to explicit user preferences.

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🔧 Solution in this Paper:

→ Introduces "preference discerning" - a new paradigm that uses LLMs to generate explicit user preferences from reviews and item data.

→ Implements Mender (Multimodal Preference Discerner) that fuses pre-trained language encoders with generative retrieval.

→ Uses cross-attention mechanism to condition recommendations on generated preferences in natural language.

→ Enables dynamic steering of recommendations through user-specified preferences.

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💡 Key Insights:

→ Explicit preference modeling consistently improves recommendation quality

→ Fine-grained steering capabilities emerge naturally from training

→ Larger language models significantly improve preference understanding

→ Models struggle with sentiment following without explicit training

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📊 Results:

→ Up to 45% relative improvement in recommendation performance

→ Achieves state-of-the-art results on preference-based recommendations

→ Successfully generalizes to new user sequences not seen during training