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About this paper
Hitesh Sagtani, Olivier Jeunen, Aleksei Ustimenko
Researchers from ShareChat developed a method called Learning-to-Rank with Nested Feedback, which integrates secondary-level user engagement signals directly into primary content ranking models for hierarchical interfaces. This approach, formalizing the direct summation of nested feedback, led to statistically significant improvements in online engagement, including an 11.23% increase in L2 transitions and a 0.5% increase in sessions on their platform.
Key insights
- For nested feed structures where the 2nd-level ranking is fixed, directly summing (rather than discounting) 2nd-level feedback into the 1st-level relevance signal is theoretically the optimal approach.
- Incorporating deeper, nested user engagement provides a more comprehensive and accurate representation of an item's true value, leading to more effective primary content ranking decisions.
- Optimizing for overall user value across both feed levels, rather than isolated level-specific objectives, is crucial for maximizing user satisfaction and platform engagement in hierarchical content presentations.
Summary by alphaXiv.
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