Hyunsoo Kim  Jongwuk Lee

10.5626/JOK.2025.52.6.548

Recommender System model merging multi-domain learning Language Model knowledge distillation

Recent research in recommender systems has increasingly focused on leveraging pre-trained large language models (LLMs) to effectively understand the natural language information associated with recommendation items. While these LLM-based recommender systems achieve high accuracy, they have a limitation in that they require training separate recommendation models for each domain. This increases the costs of storing and inferring multiple models and makes it difficult to share knowledge across domains. To address this issue, we propose an LLM-based recommendation model that effectively operates across diverse recommendation domains by applying task vector-based model merging. During the merging process, knowledge distillation is utilized from individually trained domain-specific recommendation models to learn optimal merging weights. Experimental results show that our proposed method improves recommendation accuracy by an average of 2.75% across eight domains compared to recommender models utilizing existing model merging methods, while also demonstrating strong generalization performance in previously unseen domains.