Search Query Vector Space for Tail Queries
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Solution Overview
Problem
Existing electronic document search engines struggle to provide effective related search queries and spell corrections for uncommon search queries, known as 'tail queries,' which often result in low customer conversion rates.
Innovation Solution
The methodology involves converting previous search queries into a document vector space using a machine learning model, allowing for the suggestion of similar search queries and the correction of misspellings by comparing user queries to a library of n-tuple word mappings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional search query suggestion methods are used, then common search queries can be handled effectively, but tail queries (uncommon search queries) result in low customer conversion rates
Solution Approach 1:
The patent introduces an intermediary representation (document vector space) that mediates between the search query and the document collection. By converting tail queries into document vectors and comparing them with document vectors in the collection, the system can find relevant documents even for uncommon queries, thereby improving customer conversion rates while maintaining adaptability.
Solution Approach 2:
The patent transforms the search query from its original textual form into a different parameter representation (document vector) in a mathematical space. This parameter change allows for more effective comparison and matching with document vectors, enabling the system to handle tail queries more effectively and improve conversion rates.
2Measurement precision
If spell correction is performed using traditional methods, then common misspellings can be corrected, but accuracy decreases for uncommon words and phrases
Solution Approach 1:
The patent uses document vectors as an intermediary to improve spell correction accuracy. Instead of relying solely on traditional spell checkers that may not recognize uncommon words, the system compares the vector representation of the query with document vectors to infer the intended meaning, thereby maintaining high accuracy for both common and uncommon words.
Solution Approach 2:
The patent replaces traditional mechanical spell-checking methods with a vector-based semantic comparison approach. By substituting the mechanical rule-based spell checker with a vector similarity comparison in document space, the system achieves better accuracy for uncommon words and phrases while maintaining versatility.
3Productivity
If search engines focus on improving results for tail queries, then customer conversion rates improve, but system complexity increases
Solution Approach 1:
The patent creates a universal document vector space that serves multiple functions: it is used for both search query matching and spell correction. This multi-functional approach allows the system to improve handling of tail queries and increase customer conversion rates without proportionally increasing system complexity, as the same vector space and comparison mechanisms serve multiple purposes.
Data Source
AI summary
Systems and methods for providing suggestions responsive to search queries for a search engine are disclosed. Such suggestions may include one or more related search queries and/or a spell correction. Related search queries may be provided by converting the search query into a document vector space, determining documents that are similar to the query in the vector space, and determining prior search queries to which those similar documents are responsive. Spell corrections may be provided by comparing n-tuple word combinations in the search query to a library of correct n-tuple word combinations and making appropriate corrections.