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.
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
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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.