  Personalized news recommendation aims to provide attractive articles for
readers by predicting their likelihood of clicking on a certain article. To
accurately predict this probability, plenty of studies have been proposed that
actively utilize content features of articles, such as words, categories, or
entities. However, we observed that the articles' contextual features, such as
CTR (click-through-rate), popularity, or freshness, were either neglected or
underutilized recently. To prove that this is the case, we conducted an
extensive comparison between recent deep-learning models and naive contextual
models that we devised and surprisingly discovered that the latter easily
outperforms the former. Furthermore, our analysis showed that the recent
tendency to apply overly sophisticated deep-learning operations to contextual
features was actually hindering the recommendation performance. From this
knowledge, we design a purposefully simple contextual module that can boost the
previous news recommendation models by a large margin.
