  The advent of personalized news recommendation has given rise to increasingly
complex recommender architectures. Most neural news recommenders rely on user
click behavior and typically introduce dedicated user encoders that aggregate
the content of clicked news into user embeddings (early fusion). These models
are predominantly trained with standard point-wise classification objectives.
The existing body of work exhibits two main shortcomings: (1) despite general
design homogeneity, direct comparisons between models are hindered by varying
evaluation datasets and protocols; (2) it leaves alternative model designs and
training objectives vastly unexplored. In this work, we present a unified
framework for news recommendation, allowing for a systematic and fair
comparison of news recommenders across several crucial design dimensions: (i)
candidate-awareness in user modeling, (ii) click behavior fusion, and (iii)
training objectives. Our findings challenge the status quo in neural news
recommendation. We show that replacing sizable user encoders with
parameter-efficient dot products between candidate and clicked news embeddings
(late fusion) often yields substantial performance gains. Moreover, our results
render contrastive training a viable alternative to point-wise classification
objectives.
