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arXiv · 2506.09209

Revisiting Graph Projections for Effective Complementary Product Recommendation

Leandro Anghinoni·Pablo Zivic·Jorge Adrian Sanchez
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Abstract

Complementary product recommendation is a powerful strategy to improve customer experience and retail sales. However, recommending the right product is not a simple task because of the noisy and sparse nature of user-item interactions. In this work, we propose a simple yet effective method to predict a list of complementary products given a query item, based on the structure of a directed weighted graph projected from the user-item bipartite graph. We revisit bipartite graph projections for recommender systems and propose a novel approach for inferring complementarity relationships from historical user-item interactions. We compare our model with recent methods from the literature and show, despite the simplicity of our approach, an average improvement of +43% and +38% over sequential and graph-based recommenders, respectively, over different benchmarks.

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Leandro Anghinoni, Pablo Zivic, Jorge Adrian Sanchez. 2025-06-10. Revisiting Graph Projections for Effective Complementary Product Recommendation. https://arxiv.org/abs/2506.09209

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