mlsanityData to 14 Oct 2026

CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems

arXiv 2506.19993 ↗First posted 24 Jun 2025cs.IR cs.LGcited by 3

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Abstract (arXiv)

Recommender systems play a pivotal role in providing relevant content to users. With the rapid development of large language models (LLMs), researchers have begun utilizing LLMs to build more powerful recommender systems. However, existing approaches that focus on aligning LLMs with recommendation tasks do not fully leverage their sequential information processing capabilities, leading to suboptimal performance. In this paper, we propose a novel system called compressed vocabulary expansion (CoVE). In CoVE, each item is assigned a unique ID within the expanded vocabulary. Our framework effectively capitalizes on sequence understanding abilities of LLMs, significantly enhancing their performance on recommendation tasks. Additionally, we compress the embedding layer, making CoVE practical for large-scale industrial applications. The effectiveness and performance of CoVE are demonstrated through comprehensive experiments on multiple recommendation datasets and comparisons with prior works. Our code can be found at https://github.com/HaochenZhang717/CoVE-official-Repo.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 68 of its 113 references that are in the map. First posted on arXiv 24 Jun 2025.

Builds on

68 of 113 references.

References · 68

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaborationcited by 6
  2. 2026Implicit Reasoning for Large Language Model-based Generative Recommendationcited by 4
  3. 2025Catalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendationcited by 1