Efficient incorporation of new interactions in graph recommenders via folding-in

Viacheslav Yusupov, Nikita Sukhorukov, Evgeny Frolov

Abstract: Graph-based recommender systems have emerged as a powerful paradigm for personalized recommendations. However, their reliance on full model retraining to incorporate new users or new interactions creates scalability barriers. The task becomes infeasible in real-life recommender systems due to excessive time and resource costs involved. To address this limitation, we propose a fast and efficient method for updating graph-based recommender models without full model retraining on new data. Instead of changing all weights, we modify only small share of user representations who have new interactions. Our approach achieves a remarkable speedup of 700x over conventional model retraining approaches, drastically reducing computational overhead while maintaining the accuracy of the recommendations. Furthermore, we integrate our method into a multi-representation architecture that combines graph- and sequential-based methods to capture different user and item representations. Extensive experiments on diverse datasets demonstrate that our approach achieves state-of-the-art recommendation accuracy while maintaining the efficiency of incremental updates, outperforming existing methods in both speed and quality.

External IDs:dblp:journals/umuai/YusupovSF26