OM DENNE EPISODE
In part two of the Data Skeptic Recommender Systems season finale, Kyle asks a deceptively difficult question: what should recommender systems actually optimize for? Drawing on conversations from across the season, the episode explores engagement, filter bubbles, popularity bias, fairness, human curation, embeddings, and the growing role—and risks—of large language models in shaping what gets recommended to us.
Engelsk
USA
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ANDRE EPISODE I DENNE PODKAST
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Ansvarsfraskrivelse: Podkasten og kunstværket, det finnes indlejret på denne siden, er fra Kyle Polich, som tilhører dens eier og ikke er tilknyttet eller godkjent av Listen Notes, Inc.
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