Understanding the Relation of User and News Representations in Content-Based Neural News Recommendation

@article{Moller2022UnderstandingTR,
  title={Understanding the Relation of User and News Representations in Content-Based Neural News Recommendation},
  author={Lucas Moller and Sebastian Pad{\'o}},
  journal={ArXiv},
  year={2022},
  volume={abs/2207.14704}
}
A number of models for neural content-based news recommendation have been proposed. However, there is limited understanding of the relative importances of the three main components of such systems (news encoder, user encoder, and scoring function) and the trade-offs involved. In this paper, we assess the hypothesis that the most widely used means of matching user and candidate news representations is not expressive enough. We allow our system to model more complex relations between the two by… 
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