Metadata Embeddings for User and Item Cold-start Recommendations
- Resource Type
- Working Paper
- Authors
- Kula, Maciej
- Source
- Subject
- Computer Science - Information Retrieval
H.3.3
- Language
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.