Compositional Inductive Recommenders

Modern recommender systems must serve ever-growing catalogs, including items unseen during training. This challenges models based on ID embeddings, which work well for observed items but do not generalize to new, “cold” items. We advocate for modeling item cold start as a generalization problem and formalize the induction loss allowing recommenders to generalize on cold items. We show that, around a local optimum of the standard recommendation loss, the induction loss is controlled by the reconstruction error between each item’s embedding and its reconstruction from its content-similars. Motivated by this analysis, we prove that the Softmax-Hull Embeddings (SHE), which represent each item as a content-weighted combination of neighboring item embeddings, provide the non-parametric construction that is optimal for generalization on cold items. We further show that the generalization capabilities of SHE are related to the alignment between the content representations and the item embeddings. We give empirical support to our work through recommendation experiments, demonstrating that SHE improves the overall accuracy of several ID-embedding models, simultaneously yielding the expected strong gains on cold items, and outperforming existing approaches for cold-start recommendation.