We study the problem of inferring substitutable and complementary items, which underpins applications such as alternative and follow-up purchase suggestions. Existing approaches typically learn from behavior-derived item-item associations using GNNs or leverage item content alone. However, these methods often overlook two key challenges: (i) user behaviors (e.g., co-view/co-purchase) only provide noisy weak supervision, and (ii) behavior signals are long-tailed, leaving many items with sparse associations. We propose MMSC, a self-supervised multi-modal relational representation learning framework that combines a multi-modal foundation model adapted to encode item metadata and a self-supervised denoising module that learns relationship-aware representations from noisy user behaviors, unified by a hierarchical aggregation mechanism. We further use LLM-assisted supervision to mitigate noise in behavior-derived supervision during training. Experiments on five real-world datasets show that MMSC consistently outperforms existing baselines by 26.1% for substitutable and 39.2% for complementary item inference, while remaining effective for cold-start items.

In e-commerce recommender systems, providing product suggestions to customers that are often bought together, which is called “complementary recommendation,” not only improves customer experience but also boosts business impact. However, in practice, it is highly challenging to efficiently extract the complementary relations between the items due to noisy and low coverage of the co-purchased records in transaction datasets. To address these challenges, graph neural networks (GNN) have been increasingly adopted in complementary item recommendations thanks to their capabilities in integrating side-information and topological structures to extract these complex item relationships. However, most existing GNN-based methods fall short in learning better product complementary representation since they often utilize a simple one-to-one product-to-vector mapping strategy, which fails to describe the transitive logic of complementary items. To overcome this challenge, we propose a new GNN model called transitivity-encoded graph attention networks (TransGAT). To our knowledge, TransGAT is the first method that extends representation space by encoding the behavioral direction into embedding space in GNN and enabling mutual relationship extraction between complementary items. In order to better extract customer's intrinsic behavioral information, we further adopt the substitute information as the guidance by jointly learning complements and substitutes graphs and coupling them together. Moreover, several self-supervised data augmentation strategies are incorporated in our approach. Through evaluations on three real-world datasets, TransGAT consistently surpasses contemporary benchmarks, showcasing its prowess in complementary item recommendations.