---
title: "Adaptive and Representative Multi-Interest Modeling for"
description: "The authors propose LARMI, a framework in which an LLM reads the titles of items in a user's history and splits them into… 44 references."
authors: ["Ziyan Wang", "Yingpeng Du", "Tianjun Wei", "Haoyan Chua", "Jieyi Bi", "Jie Zhang", "Zhu Sun"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.117/"
doi: "10.18653/v1/2026.findings-acl.117"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2026-adaptive-representative-multi-interest-modeling/"
---
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# Adaptive and Representative Multi-Interest Modeling for Recommendation with Large Language Model

**Authors:** [Ziyan Wang](https://md.research.elamur.ai/free/en/authors/ziyan-wang/), [Yingpeng Du](https://md.research.elamur.ai/free/en/authors/yingpeng-du/), [Tianjun Wei](https://md.research.elamur.ai/free/en/authors/tianjun-wei/), Haoyan Chua, Jieyi Bi, [Jie Zhang](https://md.research.elamur.ai/free/en/authors/jie-zhang/) and [Zhu Sun](https://md.research.elamur.ai/free/en/authors/zhu-sun/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.117/) · [PDF](https://aclanthology.org/2026.findings-acl.117.pdf) · [DOI 10.18653/v1/2026.findings-acl.117](https://doi.org/10.18653/v1/2026.findings-acl.117) · [OpenAlex](https://openalex.org/W7166819348)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Multi-interest recommenders give each user several representation vectors. Most of them group items with heuristics such as embedding similarity or co-occurrence, and these heuristics break down when interactions are sparse or side information is incomplete. The authors propose LARMI, a framework in which an LLM reads the titles of items in a user's history and splits them into semantic interest groups.

The number and breadth of these LLM groups is unpredictable. To handle this, an attention-based alignment module sharpens overly broad groups and merges overly narrow ones into collaborative interests produced by a capsule network.

A single user's history also gives only a partial view of how items relate. To widen it, LARMI clusters real users with similar tastes and solves a max covering problem to pick compact but item-spanning synthetic users for LLM analysis. Contrastive learning then pulls items together within an interest and pushes different interests apart.

On three real-world datasets, LARMI outperforms single- and multi-interest baselines. The work shows a way to control LLM semantics for interest modeling instead of treating the LLM as a black box.

## Key points
- **Two levels of modeling.** At the user-individual level, prompt-based LLM clustering is aligned with capsule-network interests. At the user-crowd level, synthetic users are selected through max covering problem (MCP) optimization.
- **Best number of interests.** The optimal K is 4 on all three datasets, which matches the average number of clusters the LLM produces.
- **MCP solver comparison.** The MCP solver (Wang et al., 2022) beats a greedy selection variant (L-gre). Both beat a variant that does not select representative synthetic users.
- **Adaptive granularity.** Cluster coarseness correlates positively with the alignment module's attention concentration, which suggests the model adapts to clusters of different breadth.
- **Case study and limitations.** In a case study on the Beauty dataset (17 items, 4 interests), the baseline MIND shows overlapping, collapsed interests, while LARMI's interests stay distinct. The listed limitations are:
  - dependence on rich item metadata;
  - reliance on external LLM APIs;
  - higher inference cost than lightweight recommendation models.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Nanyang Technological University](https://md.research.elamur.ai/free/en/universities/nanyang-technological-university/) — University, Singapore, Singapore · 156 works · 25,644 citations
- [Singapore University of Technology and Design](https://md.research.elamur.ai/free/en/universities/singapore-university-of-technology-and-design/) — University, Singapore, Singapore · 49 works · 11,074 citations

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🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works

## Cite

Cite as: Ziyan Wang, Yingpeng Du, Tianjun Wei, Haoyan Chua, Jieyi Bi, Jie Zhang and Zhu Sun. 2026. Adaptive and Representative Multi-Interest Modeling for Recommendation with Large Language Model. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2026adaptive,
  title = {{Adaptive and Representative Multi-Interest Modeling for Recommendation with Large Language Model}},
  author = {Ziyan Wang, Yingpeng Du, Tianjun Wei, Haoyan Chua, Jieyi Bi, Jie Zhang and Zhu Sun},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.117},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.117}
}
```

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*Updated 1 November 2026*