Diversity and Serendipity in Recommendations Vocabulary
Practice vocabulary for recommendation diversity: filter bubbles, exploration strategies, serendipitous recommendations, ILD diversity metric, and echo chamber effects.
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The '_____ bubble problem' describes how recommendation systems can trap users in a narrow range of content.
The 'filter bubble' (coined by Eli Pariser) describes how recommendation algorithms that optimize for engagement can isolate users from diverse perspectives.
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'We add 20% _____ to prevent over-specialization.' What strategy introduces variety?
'Exploration' (vs. exploitation) intentionally recommends items outside the user's established taste to discover new preferences and prevent narrowing.
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What is a 'serendipitous recommendation'?
A serendipitous recommendation is one the user would not have predicted they'd like but discovers they do — creating delight and broadening tastes.
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What does ILD stand for in recommendation diversity metrics?
ILD stands for Intra-List Distance — a metric measuring how dissimilar the recommended items are from each other. Higher ILD means a more diverse recommendation list.
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'The recommendation becomes a chamber _____.' What phenomenon is described?
An 'echo chamber' describes how a recommendation system can reinforce existing beliefs and preferences by repeatedly exposing users to similar content, reducing exposure to alternative viewpoints.