VoteGCL augments graph-based recommendation systems with high-confidence synthetic interactions generated via majority-voting LLM reranks and integrates them into graph contrastive learning to improve accuracy and reduce popularity bias.
arXiv preprint arXiv:1901.07555 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.IR 3roles
method 1polarities
use method 1representative citing papers
Cluster-based real-time out-of-batch negatives drawn from LLM media embeddings outperform industry-standard negative sampling for two-tower retrieval and cut popularity bias.
KnowSA_CKP uses comparative knowledge probing to selectively augment LLM prompts for items with knowledge gaps, improving recommendation accuracy and context efficiency.
citing papers explorer
-
VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation
VoteGCL augments graph-based recommendation systems with high-confidence synthetic interactions generated via majority-voting LLM reranks and integrates them into graph contrastive learning to improve accuracy and reduce popularity bias.
-
Real-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval
Cluster-based real-time out-of-batch negatives drawn from LLM media embeddings outperform industry-standard negative sampling for two-tower retrieval and cut popularity bias.
-
Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders
KnowSA_CKP uses comparative knowledge probing to selectively augment LLM prompts for items with knowledge gaps, improving recommendation accuracy and context efficiency.