Sep 2026· Proceedings of the 20th ACM Conference on Recommender Systems· pp. 1823-1825· 0 citations· 11 references
TL;DR
ReBait turns clickbait mitigation into a deployable choice-time layer for user agency, media literacy, and more trustworthy recommendation feedback.
Abstract
News recommenders ask users to act on a headline before they see an article. A curiosity gap can therefore earn a click without earning satisfaction, contaminating the implicit feedback used for future ranking. We present ReBait, a Chrome demonstration that adds an inspectable, ranking-agnostic intervention to five Taiwanese news feeds. It observes dynamically loaded cards, detects potential clickbait with a pinned XLM-R checkpoint, retrieves the linked article only for flagged cards, and uses Gemini 2.5 Flash to suggest a grounded, neutral headline. A yellow marker and hover comparison preserve the publisher’s title and click target while giving readers information before the click. ReBait turns clickbait mitigation into a deployable choice-time layer for user agency, media literacy, and more trustworthy recommendation feedback.
This work reimagines recommender systems not merely as engines of engagement, but as accountable infrastructures that uphold democratic values that affect both individuals and society at large.
Aishwarya Satwani· Proceedings of the 20th ACM...· 0 citations
News publishers usually present recommendations in fixed locations outside the main reading flow, where engagement tends to be low, and maintaining high-quality recommendations at scale is expensive. Although research has improved what is recommended, the way recommendations are presented has changed little. This demon...
Piriyakorn Piriyatamwong, Satvik Garg· Proceedings of the 20th ACM...· 0 citations
Personalized recommenders learn mostly from implicit signals such as clicks and a few coarse topic ratings. These signals are a narrow channel: a reader cannot easily ask for “more soccer World Cup coverage this month and less celebrity news,” and today’s systems give little indication of whether such a request was und...
Mahamudul Hasan· Proceedings of the 20th ACM...· 0 citations
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.
Florian Debaene, Loic De Langhe, Orphée De Clercq et al.· International Conference on...· 0 citations
The integration of generative AI into web search delivers synthesized answers to user queries, changing how people navigate and assess information, while raising concerns about the downstream impacts on publishers who supply the underlying content. We conduct a preregistered field experiment (N=1,100) on Google Search,...
Stephanie T. Wang, Jeffrey Gleason, Y. Bart et al.· 0 citations
Carousel interfaces are widely used in modern recommendation systems. Unlike traditional interfaces that present a single ranked list, carousels simultaneously present several ranked lists to the user, as horizontally swipeable rows stacked on top of each other. In this design, the rankings are closely tied to the two-...
Jingwei Kang· Proceedings of the 20th ACM...· 0 citations
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