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On many digital news platforms, such as news aggregation apps or social media feeds, recommendations are not random but shaped by continuous observation of user behavior. When a user clicks on a particular news article, the platform records this interaction as a signal of interest. Repeated clicks on similar topics, such as politics, sports, or technology, help the system build a user profile. Reading history further refines this profile by identifying preferred themes, sources, and even writing styles. For example, if a user frequently reads long-form analytical pieces rather than short breaking news, the algorithm learns to prioritize in-depth articles. Time spent on each article is also a crucial metric; longer reading duration often indicates higher engagement, prompting the system to suggest comparable content in the _____ ______ ____ ______ __________ _________ ______ _____ ________.
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