  In news recommendation systems, reducing popularity bias is essential for
delivering accurate and diverse recommendations. This paper presents POPK, a
new method that uses temporal-counterfactual analysis to mitigate the influence
of popular news articles. By asking, "What if, at a given time $t$, a set of
popular news articles were competing for the user's attention to be clicked?",
POPK aims to improve recommendation accuracy and diversity. We tested POPK on
three different language datasets (Japanese, English, and Norwegian) and found
that it successfully enhances traditional methods. POPK offers flexibility for
customization to enhance either accuracy or diversity, alongside providing
distinct ways of measuring popularity. We argue that popular news articles
always compete for attention, even if they are not explicitly present in the
user's impression list. POPK systematically eliminates the implicit influence
of popular news articles during each training step. We combine counterfactual
reasoning with a temporal approach to adjust the negative sample space,
refining understanding of user interests. Our findings underscore how POPK
effectively enhances the accuracy and diversity of recommended articles while
also tailoring the approach to specific needs.
