ProMax uses dense retrieval and dual distribution reshaping on LLM-derived profiles to guide recommender models toward preferences for unseen items, substantially boosting base model performance on public datasets.
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Pith papers citing it
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2026 4representative citing papers
Red-Rec uses AI-initiated summaries and low-effort option selection to help users with vague intent explore more broadly and with higher serendipity than user-initiated chat while requiring less typing.
SpecTran applies a spectral-aware transformer adapter with learnable position encoding to aggregate informative components across the full spectrum of LLM embeddings, yielding 9.17% average gains on sequential recommendation tasks.
citing papers explorer
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ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender Systems
ProMax uses dense retrieval and dual distribution reshaping on LLM-derived profiles to guide recommender models toward preferences for unseen items, substantially boosting base model performance on public datasets.
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From Passive Feeds to Guided Discovery: AI-Initiated Interaction for Vague Intent in Content Exploration
Red-Rec uses AI-initiated summaries and low-effort option selection to help users with vague intent explore more broadly and with higher serendipity than user-initiated chat while requiring less typing.
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SpecTran: Spectral-Aware Transformer-based Adapter for LLM-Enhanced Sequential Recommendation
SpecTran applies a spectral-aware transformer adapter with learnable position encoding to aggregate informative components across the full spectrum of LLM embeddings, yielding 9.17% average gains on sequential recommendation tasks.
- BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models