How does negative user feedback (e.g., 'Not Interested') directly impact the visibility of related titles?

Negative user feedback, such as a 'Not Interested' selection, directly decreases the visibility of related titles by negatively influencing the algorithm's perception of user preference and content relevance. When a user indicates 'Not Interested' in a specific title, the algorithm interprets this as a signal that the user is unlikely to enjoy similar content. As a result, the algorithm reduces the frequency with which related titles are recommended to that user. 'Related' titles are identified based on various factors, including shared genres, actors, keywords, and thematic elements. For example, if a user selects 'Not Interested' on a particular horror movie, the algorithm may decrease the visibility of other horror movies or movies featuring the same actors, preventing them from being prominently displayed in the user's recommendations. This negative feedback loop can also indirectly affect the visibility of these titles for other users with similar viewing patterns, as the algorithm adjusts its overall recommendations based on aggregated user feedback. The impact is not immediate elimination, but rather a subtle demotion in the ranking and frequency of recommendations, resulting in reduced visibility.