REVIEW 5 cited by
LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Contemporary recommendation systems predominantly rely on ID embedding to capture latent associations among users and items. However, this approach overlooks the wealth of semantic information embedded within textual descriptions of items, leading to suboptimal performance and poor generalizations. Leveraging the capability of large language models to comprehend and reason about textual content presents a promising avenue for advancing recommendation systems. To achieve this, we propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge. We address computational complexity concerns by utilizing pretrained LLMs as item encoders and freezing LLM parameters to avoid catastrophic forgetting and preserve open-world knowledge. To bridge the gap between the open-world and collaborative domains, we design a twin-tower structure supervised by the recommendation task and tailored for practical industrial application. Through experiments on the real large-scale industrial dataset and online A/B tests, we demonstrate the efficacy of our approach in industry application. We also achieve state-of-the-art performance on six Amazon Review datasets to verify the superiority of our method.
Forward citations
Cited by 5 Pith papers
-
Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations
LLMs exhibit mid-layer representation advantage for recommendations; MARC compresses representations modularly to reduce costs while improving performance, as shown in a large-scale online advertising deployment.
-
Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders
MPZCH applies multi-probe linear hashing plus eviction policies to achieve zero collisions on user embeddings and higher freshness on item embeddings while keeping training and inference speeds comparable to standard methods.
-
Towards Comprehensible Recommendation with Large Language Model Fine-tuning
CURec aligns an LLM with recommendation objectives via RL, generates personalized user patterns and item-reason texts, and corrects them through a chronological loop, improving top-K recall and NDCG on MovieLens and Amazon.
-
STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning
STARec trains LLM user agents to first rank fast, then reflect on mismatches and rewrite the user profile, using teacher distillation plus GRPO; on MovieLens-1M and Amazon CDs it reportedly beats full-data baselines w...
-
GREAT: Guiding Query Generation with a Trie for Recommending Related Search about Video at Kuaishou
An LLM-based item-to-query recommender with trie-constrained decoding, plus a new dataset, reports modest gains over baselines in Kuaishou's related-search scenario.
Discussion (0). Sign in to comment.