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New curiosity metric boosts recommender system engagement and revenue

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new computational measure for curiosity, a key factor in consumer engagement and breaking filter bubbles, by formalizing the information-gap theory of curiosity into five axioms. This new measure was validated through surveys and outperforms existing proxy metrics. A curiosity-aware recommender system incorporating this measure demonstrated significant improvements in click-through rates, video views, and time spent in a large-scale online experiment on a major video-streaming platform, leading to tangible economic benefits. AI

IMPACT This research could lead to more engaging and economically beneficial recommender systems by prioritizing user curiosity.

RANK_REASON Academic paper introducing a new theoretical framework and computational measure for curiosity in recommender systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New curiosity metric boosts recommender system engagement and revenue

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Academic paper introducing a new theoretical framework and computational measure for curiosity in recommender systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Alexander Tuzhilin ·

    When Does a Recommendation Spark Curiosity? Theory, Measurement, and Field Evidence

    Curiosity, the drive to close the gap between what one knows and what one wants to know, pulls consumers beyond their familiar content, sustains engagement, and breaks them out of the filter bubbles of repetitive, relevance-only recommendations. Despite its importance, curiosity …

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