Search Result Ranker Optimization via Post-Impression Features
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Solution Overview
Problem
Existing search result rankers face challenges in optimizing search result rankings due to the impracticality of large-scale expert assessment data collection and the difficulty in determining relevant post-impression features like dwell time for personalizing search results.
Innovation Solution
A computerized method that retrieves query-document pairs with associated post-impression features, generates a weight vector, and uses a performance metric to optimize the search result ranker, incorporating features such as click and dwell times to improve ranking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert assessment data is collected for machine learning, then search result relevancy improves, but data collection becomes impractical and complicated at large scale
Solution Approach 1:
The patent uses post-impression features (clicks, dwell time) as proxies or copies of expert assessment data. Instead of collecting actual expert ratings for large-scale personalization, the system infers relevancy from user interaction behaviors that replicate expert judgment at scale.
Solution Approach 2:
The system allows users to self-assess document relevancy through their interaction behaviors (clicks, dwell time). Rather than requiring external expert assessors, the user's own actions provide the assessment data needed for machine learning optimization.
2Measurement precision
If post-impression features like dwell time are used to improve ranking, then relevancy determination improves, but difficulty in determining appropriate feature values increases
Solution Approach 1:
The patent uses post-impression features as feedback signals from user behavior. Dwell time, clicks, and other interaction metrics provide feedback on document relevancy, which is then fed back into the machine learning model to optimize the ranking formula iteratively.
Solution Approach 2:
The system changes the parameters used for relevancy assessment from traditional factors to post-impression features. Instead of relying on static document characteristics, the patent dynamically uses user interaction parameters (clicks, dwell time) that change based on actual user behavior patterns.
Data Source
AI summary
A computerized method for optimizing search result rankings obtained from a search result ranker has the steps of retrieving a first set of query-document pairs, each query-document pair of the first set having an associated post-impression features vector; generating a weight vector having a number of weights corresponding to a number of post-impression features in each of the post-impression feature vector of the first set; generating a target function by using the weight vector and the post-impression features vectors of the first set; using a performance metric associated with the target function, optimizing the weights of the weight vector using the first set of query-document pairs to obtain an optimized target function; optimizing the search result ranker using the optimized target function; and using the optimized search result ranker to rank search results.