RAH! RecSys–Assistant–Human: A Human-Centered Recommendation Framework With LLM Agents
Open accessAbstract
The rapid evolution of the web has led to an exponential growth in content. Recommender systems play a crucial role in human–computer interaction (HCI) by tailoring content based on individual preferences. Despite their importance, challenges persist in balancing recommendation accuracy with user satisfaction, addressing biases while preserving user privacy, and solving cold-start problems in cross-domain situations. This research argues that addressing these issues is not solely the recommender systems’ responsibility, and a human-centered approach is vital. We introduce the recommender system, assistant, and human (RAH) framework, an innovative solution with large language model (LLM)-based agents such as perceive, learn, act, critic, and reflect, emphasizing the alignment with user personalities. The framework utilizes the learn-act-critic loop and a reflection mechanism for improving user alignment. Using the real-world data, our experiments demonstrate the RAH framework's efficacy in various recommendation domains, from reducing human burden to mitigating biases and enhancing user control. Notably, our contributions provide a human-centered recommendation framework that partners effectively with various recommendation models.
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The pre-trained knowledge and strong generalization capabilities of LLMs facilitate better knowledge transfer across domains, addressing cold-start with minimal additional training.
“Furthermore, the pre-trained knowledge and strong generalization capabilities of LLMs facilitate better knowledge transfer across domains, addressing persistent challenges such as cold-start (Shu et al., 2024) with minimal additional training.”
From A Survey on LLM-powered Agents for Recommender Systems (2025) · cites this paper as Shu et al., 2024
Section 3.3 says the assistant extrapolates a learned user personality across domains, alleviating cold-start, and Section 5.1 reports that assistants can reason across domains. The paper does not establish that this works with minimal additional training; Section 5.2 describes training the recommendation models on substantial interaction data and assistant-generated feedback. Our copy gives no page numbers for these passages. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe 'Video Games' dataset has validated DRDT and RAH capabilities.
“while the 'Video Games' dataset (2.8M users, 137.2K items) has validated DRDT (Wang et al., 2023b) and RAH (Shu et al., 2024) capabilities.”
From A Survey on LLM-powered Agents for Recommender Systems (2025) · cites this paper as Shu et al., 2024
Section 4 (Experiments Setting; page unavailable) says RAH was evaluated using Amazon Video Games data, and Section 5 reports its experimental results. But the paper describes filtered, cross-domain subsets, including Cross221k with 221,861 users; it does not establish that RAH was validated on a Video Games dataset of 2.8 million users and 137,200 items. DRDT is attributed to a separate citation in the citing sentence. (LLM, conf=0.87)
Experimental — accuracy not yet measured
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- Authors
- Shu, Y., Zhang, H., Gu, H., Zhang, P., Lu, T., Li, D., & Gu, N.
- Affiliations
- Fudan University (CN)
- Seattle, WA, USA
- Microsoft Research Asia, Shanghai, China
- Venue
- IEEE Transactions on Computational Social Systems
- Published
- 2024-10
- Volume
- 11
- Issue
- 5
- Pages
- 6759-6770
- ISSN
- 2329-924X (ISSN-L)
- 2373-7476
- OpenAlex
- W4400033172
- Cited by
- 66
- References
- 53
- Citations by year
- 2026: 27 · 2025: 26 · 2024: 13
- FWCI
- 17.43
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Computational Social Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61932007, 62172106, 62372113)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- LLM agents, recommender systems, reflection mechanism, recommendation bias, user privacy, cold start, cross-domain recommendation, user control, human-computer interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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