Learning through News: Bridging the Gap between Algorithmic Recommendation and Human Curation

News recommendation systems play a central role in how readers access and process current events. Most recommenders’ underlying algorithmic strategies, however, prioritize user engagement over comprehension, amplifying risks of misinformation and filter bubbles. This study investigates whether fine-grained content-based recommendation strategies favor human knowledge retention and explores how such a content-based recommendation can be operationalized using event coreference–based document modeling. To this purpose, we first measure the effect of manually curated content-based news recommendation on knowledge retention across five news topics with 126 Dutch speaking participants. Next, we investigate document retrieval by comparing a state-of-the-art event coreference resolution system for Dutch which recommends news articles based on event chains with a document similarity retrieval baseline using state-of-the-art embedding models in three increasingly more complex test settings. The results demonstrate that human-curated content-based recommendation can positively and significantly impact readers’ knowledge retention. Moreover, we show that a fine-grained coreference system can approach said level of human curation better than state-of-the-art document retrieval methods. In general, this holds potential for scalable, comprehension-oriented news recommendation.