Equivariant Learning for Out-of-Distribution Cold-start Recommendation

Recommender systems rely on user-item interactions to learn Collaborative Filtering (CF) signals and easily under-recommend the cold-start items without historical interactions. To boost cold-start item recommendation, previous studies usually incorporate item features (e.g., micro-video content features) into CF models. They essentially align the feature representations of warm-start items with CF representations during training, and then adopt the feature representations of cold-start items to make recommendations. However, cold-start items might have feature distribution shifts from warm-start ones due to different upload times. As such, these cold-start item features fall into the underrepresented feature space, where their feature representations cannot align well with CF signals, causing poor cold-start recommendation.