Metadata Embeddings for User and Item Cold-start Recommendations
This addresses the cold-start problem in recommender systems, which is a common issue for new users or items, though it appears incremental as it builds on existing matrix factorization techniques.
The paper tackles the cold-start recommendation problem by introducing a hybrid matrix factorization model that combines user and item metadata, achieving superior performance in sparse data scenarios and matching pure collaborative methods when data is abundant.
I present a hybrid matrix factorisation model representing users and items as linear combinations of their content features' latent factors. The model outperforms both collaborative and content-based models in cold-start or sparse interaction data scenarios (using both user and item metadata), and performs at least as well as a pure collaborative matrix factorisation model where interaction data is abundant. Additionally, feature embeddings produced by the model encode semantic information in a way reminiscent of word embedding approaches, making them useful for a range of related tasks such as tag recommendations.