InvariRank achieves permutation-invariant listwise reranking for LLM-based recommendations via a structured attention mask that blocks cross-candidate interactions and shared positional framing under RoPE, enabling stable rankings in one forward pass.
Large Language Models are Zero-Shot Rankers for Recommender Systems , booktitle =
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HSTU-based generative recommenders with 1.5 trillion parameters scale as a power law with compute up to GPT-3 scale, outperform baselines by up to 65.8% NDCG, run 5-15x faster than FlashAttention2 on long sequences, and improve online A/B metrics by 12.4%.
LRanker combines K-means candidate aggregation with graph-partitioned ensemble of query embeddings to improve LLM ranking accuracy and scalability on massive candidate pools, reporting 3-30% gains on RBench tasks up to 6.8M candidates.
Ocean4Rec uses offline LLM to create OCEAN profiles for items and time-decayed user profiles for request-time numeric reranking, improving NDCG@20 by 7.6% and 61.5% over base+recency in offline VOD evaluations.
A distillation technique embeds LLM-generated textual user profiles into efficient sequential recommenders without runtime LLM inference, architectural changes, or fine-tuning.
citing papers explorer
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Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
HSTU-based generative recommenders with 1.5 trillion parameters scale as a power law with compute up to GPT-3 scale, outperform baselines by up to 65.8% NDCG, run 5-15x faster than FlashAttention2 on long sequences, and improve online A/B metrics by 12.4%.
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LRanker: LLM Ranker for Massive Candidates
LRanker combines K-means candidate aggregation with graph-partitioned ensemble of query embeddings to improve LLM ranking accuracy and scalability on massive candidate pools, reporting 3-30% gains on RBench tasks up to 6.8M candidates.
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Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Reranking
Ocean4Rec uses offline LLM to create OCEAN profiles for items and time-decayed user profiles for request-time numeric reranking, improving NDCG@20 by 7.6% and 61.5% over base+recency in offline VOD evaluations.
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Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation
A distillation technique embeds LLM-generated textual user profiles into efficient sequential recommenders without runtime LLM inference, architectural changes, or fine-tuning.