Query Intent Specificity Scoring Through Vector Aggregation for Tail Queries
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Solution Overview
Problem
Conventional search engines struggle to accurately identify and provide targeted search results for tail queries due to noisy and sparse logs, large variability, and lack of information, leading to inefficient handling of user intent, especially in e-commerce platforms.
Innovation Solution
A system that generates query vectors by aggregating item listing vectors and determines similarities using cosine similarities, trains an intent specificity machine learning model to provide intent specificity scores, and uses these scores to generate tailored search results based on query-dependent or query-independent factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines use traditional search algorithms for tail queries, then the system complexity remains low, but the search result precision and relevance deteriorate due to noisy and sparse logs
Solution Approach 1:
The patent introduces query vectors and intent specificity scores as intermediary representations to bridge the gap between raw search queries and search results. By transforming queries into vectors and calculating intent specificity scores, the system achieves more precise match without significantly increasing operational complexity during query processing.
Solution Approach 2:
The system performs preliminary processing by generating query vectors and intent specificity scores before retrieving search results. This preliminary action filters and prioritizes queries, allowing the search system to focus computational resources on high-value queries and improve overall precision without proportionally increasing total system complexity.
2Productivity
If the search engine processes all queries with equal detail, then the device complexity remains manageable, but the retrieval efficiency for tail queries deteriorates due to lack of targeted processing
Solution Approach 1:
The patent applies local quality by differentiating processing intensity based on query characteristics. Tail queries with lower intent specificity scores receive different processing strategies compared to head queries, allowing the system to optimize retrieval efficiency for specific query types without uniformly increasing complexity across all queries.
Solution Approach 2:
The system dynamically adjusts processing approaches based on real-time intent specificity calculations. By making processing complexity adaptive rather than static, the system can improve retrieval efficiency for tail queries through enhanced processing when needed while maintaining manageable overall complexity through selective application.
3Reliability
If the search engine uses traditional keyword matching, then the ease of operation remains high, but the intent specificity and user experience deteriorate
Solution Approach 1:
The patent replaces traditional mechanical keyword matching with vector-based semantic understanding. By substituting simple string matching with query vector generation and similarity calculations, the system achieves more accurate intent identification while the added algorithmic complexity is managed through efficient computational approaches.
Solution Approach 2:
The system changes the fundamental parameters of query representation from discrete keywords to continuous vectors. This parameter transformation enables the system to capture semantic meaning and intent specificity, improving reliability of intent identification while managing complexity through mathematical operations that are computationally efficient at scale.
4Measurement precision
If the search engine provides comprehensive search results, then the quantity of information is high, but the precision and relevance of individual results deteriorate
Solution Approach 1:
The patent applies partial action by providing a curated subset of search results rather than all possible results. By calculating intent specificity scores and using these to filter and rank results, the system delivers a manageable number of highly relevant results, improving precision without requiring the user to process an excessive quantity of information.
Data Source
AI summary
The technology described herein relates to systems, methods, and computer storage media, among other things, for providing search query intent specificity. Embodiments may include identifying a search query performed using a search engine and generating a query vector for the search query by aggregating search result embeddings (e.g., item listing vectors) of search results from the search query. Further, in some embodiments, similarities (e.g., cosine similarities) between the query vector and the item listing vectors can be determined. As such, an intent specificity of the search query can be determined. Further, in some embodiments, the intent specificity can be used to train an intent specificity machine learning model for generating intent specificity scores for other search queries. Based on the intent specificity scores determined using the one or more trained intent specificity machine learning models, determinations can be made with respect to precision and recall, etc.