Revisiting Graph Projections for Effective Complementary Product Recommendation

2025-06-10

AI Summary

Complementary product recommendation suffers from severe data sparsity and noise in user–item interaction graphs. To address the inadequate modeling of complementary relationships in bipartite graphs, this work systematically redefines the bipartite graph projection paradigm. We propose a directed weighted projection method to explicitly capture the directional nature of complementarity; design an edge-weight recalibration mechanism to suppress noise-induced distortions; and introduce structured neighborhood aggregation coupled with a parameter-free similarity propagation strategy to enhance relational generalization under sparsity. Evaluated on multiple benchmark datasets, our approach achieves average improvements of 43% and 38% in recommendation accuracy over state-of-the-art sequential and graph neural recommender models, respectively. It significantly advances complementary item identification performance under sparse and noisy conditions.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Improving complementary product recommendation accuracy
• Addressing noisy sparse user-item interaction data
• Enhancing graph-based complementarity relationship inference

Innovation

• Projecting user-item bipartite graph to directed weighted graph
• Inferring complementarity from historical user interactions
• Improving recommendation accuracy over existing methods