On the Overlooked Significance of Underutilized Contextual Features in Recent News Recommendation Models
This addresses an incremental improvement in personalized news recommendation for readers by highlighting overlooked features.
The paper tackles the problem of underutilizing contextual features like CTR, popularity, and freshness in news recommendation models, finding that naive contextual models outperform recent deep-learning approaches and that a simple contextual module can significantly boost performance.
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.