arXiv Computation and Language October 7, 2026 By Xinfeng Wang, Jin Cui, Fumiyo Fukumoto, Yoshimi Suzuki

Enhancing High-order Interaction Awareness in LLM-based Recommender Model

Read the original on arXiv Computation and Language →

The paper introduces ELMRec, an enhanced LLM-based recommender that improves high-order interaction awareness by using whole-word embeddings to better interpret graph-constructed user-item interactions without graph pre-training. It also identifies that LLMs tend to prioritize earlier user interactions over recent ones and proposes a reranking solution. ELMRec achieves superior performance compared to state-of-the-art methods in both direct and sequential recommendation tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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