Corpus ID: 202537342

Deep Context-Aware Recommender System Utilizing Sequential Latent Context

@article{Livne2019DeepCR,
  title={Deep Context-Aware Recommender System Utilizing Sequential Latent Context},
  author={Amit Livne and Moshe Unger and Bracha Shapira and Lior Rokach},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.03999}
}
Context-aware recommender systems (CARSs) apply sensing and analysis of user context in order to provide personalized services. Adding context to a recommendation model is challenging, since the addition of context may increases both the dimensionality and sparsity of the model. Recent research has shown that modeling contextual information as a latent vector may address the sparsity and dimensionality challenges. We suggest a new latent modeling of sequential context by generating sequences of… Expand

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