Content Ranking Using Interaction Data Thresholds
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Solution Overview
Problem
Personalized content item ranking models can overfit user-interaction data, leading to poor search experiences when there is limited data, as they may overly favor pages with interaction data and negatively weight those without, resulting in suboptimal rankings.
Innovation Solution
A method that initially ranks content items using a broadly applicable scheme and then re-ranks them based on user-interaction data, prioritizing preferred items while preserving the original ranking for items with no clear user preference, using a computing device to determine relevant and irrelevant items and adjust their positions accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If personalized ranking models are used to re-rank content items based on user-interaction data, then user preference is improved, but overfitting occurs leading to poor search experience when data is limited
Solution Approach 1:
The patent applies partial action by selectively re-ranking only those content items that have sufficient user-interaction data, while leaving items with limited or no data in their original positions. This prevents overfitting to sparse data while still personalizing results where appropriate. The system determines a threshold for data sufficiency and applies personalized re-ranking only to items meeting this threshold.
Solution Approach 2:
The patent changes the weighting parameter dynamically based on data availability. When user-interaction data is sufficient, the personalized ranking weight is increased; when data is limited, the weight reverts to the original global ranking. This parameter adjustment resolves the contradiction by adapting the personalization intensity to the quality and quantity of available data.
2Adaptability or versatility
If user-interaction data is used to heavily weight preferred content items, then personalization is improved, but content items without interaction data are unfairly penalized
Solution Approach 1:
The patent introduces a counterweight mechanism where content items without sufficient user-interaction data are protected from being overly down-ranked. The system calculates a protective weight or threshold that prevents items with limited data from falling too low in the ranking, ensuring they remain visible and fair competition is maintained. This counterbalances the positive weighting applied to preferred items.
Solution Approach 2:
The patent applies different quality standards to different portions of the ranking. High-quality items with sufficient interaction data receive personalized re-ranking, while low-quality items with insufficient data maintain their original positions. This local differentiation ensures fairness by not applying the same personalization logic uniformly to all items regardless of data quality.
3Adaptability or versatility
If personalized models are applied to all content items, then user preference coverage is improved, but data sparsity leads to inaccurate rankings
Solution Approach 1:
The patent implements partial action by applying personalized ranking only to a subset of content items where user-interaction data is sufficient. Items below a certain data threshold are excluded from personalized re-ranking and remain in their original global ranking positions. This selective application maintains accuracy by avoiding personalization where data is too sparse to support reliable predictions.
Data Source
AI summary
A set of content items, such as web pages, are identified in response to a query generated by a user. The Identified content items are initially ranked using a ranking scheme. User-interaction data that describes preferences that the user may have towards some of the ranked content items is received. In order to personalize the ranking of the content items for the user, the user-interaction data is used to re-rank the ranked content items in a way that favors content items that are preferred by the user, while also preserving the initial broadly applicable ranking with respect to content items that are not preferred or that are equally preferred by the user.