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About this paper
Srivaths Ranganathan, Zihuan Diao, Bernardo Cunha, Joshua L. Moore, Robin Dumas, Murat Goksedef, Yanwei Song, Mukai Lu and 2 more
A comprehensive empirical comparison of strategies to combat feedback loops in the YouTube Music recommender system reveals that different intervention layers have distinct impacts on surfacing new releases and novel content, with uncertainty-driven exploration being most effective for increasing new-release engagement. Interventions targeting serving-time rules were found to be transient due to continuous model adaptation.
Key insights
- Serving-layer heuristics intended to boost recency are often transient, as the continuously learning recommender system adapts to and neutralizes their effects over time.
- Architectural debiasing methods, such as position/client bias towers, can effectively redistribute popularity and increase novel music engagement, but do not inherently improve the surfacing of new music releases.
- Uncertainty-driven exploration strategies, particularly those leveraging Spectral-normalized Neural Gaussian Process (SNGP) heads, consistently yielded the largest gains in new-release engagement.
- Combining interventions from different layers can lead to complex, non-additive interactions, and may sometimes counteract benefits observed from individual strategies.
Summary by alphaXiv.
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