XRec and RLMRec
These are complementary approaches to LLM-based recommendation: XRec prioritizes interpretability through explicit explanations, while RLMRec focuses on leveraging LLM representations as feature embeddings, enabling them to be combined for systems that are both semantically rich and human-understandable.
About XRec
HKUDS/XRec
[EMNLP'2024] "XRec: Large Language Models for Explainable Recommendation"
About RLMRec
HKUDS/RLMRec
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
Leverages LLM-generated user/item profiles and semantic embeddings to bridge collaborative filtering with semantic understanding, using contrastive or generative alignment to synchronize LLM representation spaces with graph-based collaborative signals. Implements a model-agnostic framework compatible with multiple graph neural network backbones (LightGCN, SGL, SimGCL, etc.) on text-attributed recommendation datasets. Built on PyTorch with pre-processed datasets (Amazon-book, Yelp, Steam) that include LLM-generated profiles, text embeddings, and sparse interaction matrices for reproducibility.
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