Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.09209 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908403886981120 |
|---|---|
| author | Anghinoni, Leandro Zivic, Pablo Sanchez, Jorge Adrian |
| author_facet | Anghinoni, Leandro Zivic, Pablo Sanchez, Jorge Adrian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09209 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revisiting Graph Projections for Effective Complementary Product Recommendation Anghinoni, Leandro Zivic, Pablo Sanchez, Jorge Adrian Information Retrieval Machine Learning 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. |
| title | Revisiting Graph Projections for Effective Complementary Product Recommendation |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2506.09209 |