NFC: a deep and hybrid item-based model for item cold-start recommendation

Abstract

New items, also called cold-start items, are introduced every day in the catalogs of numerous online systems. Due to the absence of previous preferences, recommending these items is difficult but important task for a recommender system. For this reason, the item cold-start recommendation problem still represents an interesting research topic for the community. In this work, we propose Neural Feature Combiner (NFC), a novel deep learning, item-based approach for cold-start item recommendation. The model learns to map the content features of the items into a low-dimensional hybrid embedding space. The features that compose the embeddings are then combined in order to reproduce collaborative item similarity values. We compare NFC with three variants of the same model that learn from user feedback, showing the advantages of learning from similarities in terms of accuracy and convergence time. With an extensive set of experiments on four datasets, we show that NFC outperforms several cutting edge approaches in the top-n recommendation task of cold-start items. Results in extremely cold (i.e., with a very low amount of interactions for training) and cold-warm hybrid scenarios prove that NFC effectively exploits collaborative information, leading to state-of-the-art accuracy. We finally conduct a qualitative analysis of the embeddings generated by different models, and we provide an analysis of the importance that different models assign to the input features, empirically demonstrating the robustness of the hybrid representations produced by our new model.