PEARL: Unbiased Percentile Estimation via Contrastive Learning for Industrial-Scale Livestream Recommendation
arXiv:2605.21752v1 Announce Type: new Abstract: Recommender systems trained on user interaction data are susceptible to behavioral intensity imbalance--a systematic distortion arising from heterogeneous engagement patterns across users. This imbalance skews feedback signals such that observed interactions no longer faithfully reflect true preferences, causing models to disproportionately amplify signals from highly active users while underrepresenting others, which ultimately degrades recommendation quality and robustness at scale. To address this issue, we propose a nonparametric contrastive
The proliferation of industrial-scale content platforms and the increasing sophistication of AI in recommendation systems necessitate more robust methods for handling complex user interaction data.
Improving livestream recommendation quality directly translates to enhanced user engagement, retention, and monetization for major platforms, impacting their competitive landscape and advertising revenue.
Recommendation models can now potentially better reflect true user preferences by mitigating biases from uneven behavioral intensity, leading to more equitable and effective content distribution.
- · Livestreaming platforms
- · Content creators
- · Users of recommendation systems
- · AI/ML researchers in recommender systems
- · Platforms with unsophisticated recommendation algorithms
- · Algorithms that unduly amplify highly active users
More accurate and engaging livestream recommendations lead to increased time spent on platforms.
Improved user satisfaction and discoverability could shift market share towards platforms employing advanced unbiased recommendation methodologies.
The principle of unbiased percentile estimation could be generalized to other complex data domains, improving fairness and efficacy across various AI applications.
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Read at arXiv cs.LG