Integrates contextual constraints into embedding-based recommendation models by jointly learning constraint representations with user and item embeddings, reporting improved predictive performance on matrix factorization models.
Metadata Embeddings for User and Item Cold-start Recommendations
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abstract
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.
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cs.IR 1years
2019 1verdicts
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Embedding models for recommendation under contextual constraints
Integrates contextual constraints into embedding-based recommendation models by jointly learning constraint representations with user and item embeddings, reporting improved predictive performance on matrix factorization models.