rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendation
Large language models can improve recommendation quality by reasoning explicitly over user history and candidate items - for example, extracting a user's preferences or explaining why one item fits better than another - rather than mapping history directly to a ranked list. This reasoning, however, is expensive to repeat on every ranking request and, once produced, is typically consumed once and discarded, leaving it neither reusable across future requests nor easy to inspect or correct as user tastes drift. Our insight is that reasoning does not need to be regenerated at every call if it can
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%open-webui/open-webui →
“Shared author/contributor keys: nguyen”
- LinkedLinked via arxiv author · 85%Minh Hoang Nguyen →
“rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendation”
- LinkedLinked via arxiv author · 85%Tung Le →
“rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendation”
- LinkedLinked via arxiv author · 85%Huy Tien Nguyen →
“rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendation”