Query Specificity Estimation Using Engagement Data
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Solution Overview
Problem
Existing technologies face challenges in accurately determining the specificity of user queries, particularly implicit intents, which affects the relevance of search results in e-commerce platforms, as current methods rely heavily on query length, IDF, and term specificity without considering query semantics and user engagement data.
Innovation Solution
An engagement-based estimation method is introduced, utilizing user engagement data and specificity scores to classify query specificity, incorporating constraints to normalize queries, propagate scores across equivalent and sibling queries, and employing a specificity classifier to generate scores for queries with insufficient data, thereby improving the relevance of search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If query length, IDF, and term specificity are used to determine query specificity, then the measurement can be implemented with simple metrics, but the accuracy of query specificity estimation deteriorates due to ignoring query semantics and user engagement data
Solution Approach 1:
The patent segments the query specificity estimation into multiple components: (1) query normalization to handle equivalent queries, (2) engagement-based specificity scoring using user behavior data, (3) propagation of scores across related queries, and (4) machine learning classification. This segmentation allows each component to be optimized independently while improving overall accuracy.
Solution Approach 2:
The patent introduces user engagement data as an intermediary element that mediates between the query text and the specificity score. Instead of directly measuring specificity from query terms alone, the system uses user engagement patterns (clicks, purchases, views) as an intermediate signal that reflects actual query intent and specificity, thereby improving estimation accuracy.
2Measurement precision
If user engagement data and semantic analysis are incorporated to improve query specificity estimation accuracy, then the relevance of search results improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent performs query normalization and engagement data aggregation in advance, before the actual search query is processed. By pre-processing queries to identify equivalent forms and pre-aggregating user engagement statistics, the system reduces the computational burden during real-time search operations, balancing accuracy with efficiency.
Solution Approach 2:
The system uses historical user engagement data to automatically train and refine the machine learning classifier for specificity estimation. The model continuously improves its accuracy by learning from past user behavior patterns without requiring manual intervention, thereby managing complexity through automated self-optimization.
3Adaptability or versatility
If specificity scores are propagated across equivalent and sibling queries, then the coverage of specificity estimation improves, but the risk of error propagation increases
Solution Approach 1:
The patent implements a feedback mechanism where propagated specificity scores are used to train the machine learning classifier, which in turn generates improved specificity scores that can feedback into the propagation process. This iterative feedback loop allows the system to learn from propagated scores while correcting errors, thereby maintaining reliability while expanding coverage.
Solution Approach 2:
The system dynamically adjusts the propagation parameters and thresholds based on query characteristics and data quality. By changing parameters such as propagation depth, similarity thresholds, and weighting factors, the system optimizes the balance between coverage and accuracy for different query types and contexts.
Data Source
AI summary
A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform certain operations. The operations can include generating a first specificity score for a first query. The operations also can include propagating the first specificity score for the first query to generate a second specificity score for a second query. The operations additionally can include training a machine-learning classifier at least based on the first query and the second query. The operations further can include generating, using the machine-learning classifier, a third specificity score for a third query. Other embodiments are described.