MergeRec: Model Merging for Data-Isolated Cross-Domain Sequential Recommendation

📝 Original Info

• Title: MergeRec: Model Merging for Data-Isolated Cross-Domain Sequential Recommendation
• ArXiv ID: 2601.01753
• Date: 2026-01-05
• Authors: Hyunsoo Kim, Jaewan Moon, Seongmin Park, Jongwuk Lee

📝 Abstract

Modern recommender systems trained on domain-specific data often struggle to generalize across multiple domains. Cross-domain sequential recommendation has emerged as a promising research direction to address this challenge; however, existing approaches face fundamental limitations, such as reliance on overlapping users or items across domains, or unrealistic assumptions that ignore privacy constraints. In this work, we propose a new framework, MergeRec, based on model merging under a new and realistic problem setting termed data-isolated cross-domain sequential recommendation, where raw user interaction data cannot be shared across domains. MergeRec consists of three key components: (1) merging initialization, (2) pseudo-user data construction, and (3) collaborative merging optimization. First, we initialize a merged model using training-free merging techniques. Next, we construct pseudo-user data by treating each item as a virtual sequence in each domain, enabling the synthesis of meaningful training samples without relying on real user interactions. Finally, we optimize domain-specific merging weights through a joint objective that combines a recommendation loss, which encourages the merged model to identify relevant...

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