Learning through News: Bridging the Gap between Algorithmic Recommendation and Human Curation
TL;DR
The results demonstrate that human-curated content-based recommendation can positively and significantly impact readers’ knowledge retention and show that a fine-grained coreference system can approach said level of human curation better than state-of-the-art document retrieval methods.
Abstract
News recommendation systems play a central role in how readers access and process current events. Most recom-menders’ 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.