Search Query Intent Specificity Through Vector Similarity Scoring
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Solution Overview
Problem
Conventional search engines struggle to accurately identify and provide targeted search results for tail queries due to their inability to consider user intent specificity, leading to inefficient and imprecise retrieval and ranking of search results, especially in e-commerce platforms where user interactions are sparse and varied.
Innovation Solution
A system that generates query vectors by aggregating item listing vectors and determines similarities using cosine distances, trains an intent specificity machine learning model to generate scores for search queries, and uses these scores to provide 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, then they can process search queries, but they fail to accurately identify user intent and provide targeted search results for tail queries
Solution Approach 1:
The patent changes the parameter representation from traditional keyword matching to vector space representation. Query vectors and item listing vectors are generated using embedding models, and similarity is measured using cosine distance. This parameter transformation enables the system to capture semantic meaning and user intent specificity, resolving the contradiction between intent identification accuracy and retrieval efficiency.
Solution Approach 2:
The patent introduces query vectors and item listing vectors as intermediary representations between search queries and search results. These vectors serve as mediators that capture semantic relationships and enable precise intent identification. The similarity calculation between vectors acts as an intermediary mechanism that bridges the gap between query processing and result retrieval.
2Manufacturing precision
If search engines provide search results without considering intent specificity, then they can return results quickly, but the relevance and precision of results deteriorate
Solution Approach 1:
The patent performs preliminary actions by pre-computing query vectors and item listing vectors using embedding models. These vectors are generated in advance and stored, enabling fast similarity calculations during actual search queries. This preliminary processing reduces real-time computation overhead while maintaining high precision in intent identification and result relevance.
3Adaptability or versatility
If search engines treat all queries uniformly, then they can simplify processing, but they cannot optimize for different query types such as tail queries with sparse user interactions
Solution Approach 1:
The patent introduces dynamic adaptability through the intent specificity machine learning model that adjusts search behavior based on query characteristics. The system dynamically determines intent specificity scores and modifies retrieval strategies accordingly. This dynamic approach enables the system to adapt to different query types (head queries, torso queries, tail queries) without requiring separate rigid processing pipelines, thus achieving versatility while controlling complexity.
4Reliability
If search engines perform comprehensive search processing, then they can improve result quality, but unnecessary I/O operations increase physical wear on storage components
Solution Approach 1:
The patent extracts and utilizes only the necessary information from search results through vector embeddings. Instead of processing and storing complete result datasets, the system extracts semantic representations (vectors) that capture essential meaning. This extraction approach reduces data volume and I/O operations while maintaining search result quality, thereby reducing physical wear on storage components.
Data Source
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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.