Adaptive Query Suggestion for Search Relevance
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Solution Overview
Problem
Users often face difficulties in composing queries that yield relevant and authoritative search results, with existing query suggestion techniques either suggesting irrelevant alternatives or annoying users with unnecessary suggestions.
Innovation Solution
Adaptive query suggestion analyzes user-submitted queries to determine their difficulty and only provides alternative suggestions when necessary, using feature extraction and ranking to present the most effective candidate queries, which are identified through click-through data and transformation operations to account for variations in query intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If query suggestion techniques suggest alternate queries for all user-submitted queries, then the relevance of suggested queries may be improved, but users are annoyed by unnecessary suggestions
Solution Approach 1:
The system changes the parameter of query difficulty assessment by analyzing specific features of user-submitted queries (such as query length, term frequency, and semantic complexity) to dynamically determine whether suggestions should be provided. This resolves the contradiction by adapting the suggestion behavior based on the inherent characteristics of each query rather than applying a uniform approach.
Solution Approach 2:
The system enables queries to effectively 'self-evaluate' their own difficulty level through automated analysis of query features and comparison against a database of known difficult queries. This self-service mechanism determines whether the query needs suggestions, thereby improving relevance while avoiding unnecessary suggestions that would annoy users.
2Reliability
If query suggestion techniques provide suggestions for difficult queries, then the effectiveness of search results is improved, but the complexity of the system increases
Solution Approach 1:
The system performs preliminary analysis of query features and pre-assesses query difficulty before generating suggestions. By pre-computing query characteristics and comparing them against established difficulty criteria, the system prepares in advance whether suggestions are needed, reducing the complexity of real-time decision-making while maintaining high effectiveness.
Solution Approach 2:
The system replaces complex manual query evaluation with automated computational analysis of query features. By using algorithmic assessment of query characteristics (such as term frequency, query length, and semantic analysis) rather than manual evaluation, the system achieves high effectiveness with reduced operational complexity.
3Ease of operation
If the system analyzes query difficulty and provides selective suggestions, then user experience is improved, but the processing time increases
Solution Approach 1:
The system applies different levels of analysis to different queries based on their local characteristics. Simple queries receive minimal or no analysis, while queries exhibiting features of difficulty undergo more comprehensive analysis. This localized approach improves user experience by providing suggestions only when needed, while minimizing processing time for the majority of simple queries.
Solution Approach 2:
The system performs partial analysis on all queries (basic feature extraction) and excessive analysis only on queries that exhibit difficulty characteristics. This partial/excessive action strategy ensures that most queries are processed quickly with minimal analysis, while only the necessary subset receives the more time-consuming difficulty assessment and suggestion generation.
Data Source
AI summary
When a user-submitted query is received, a set of candidate queries is identified. For each of the candidate queries, features are extracted that, for each candidate query, reflect a measure of effectiveness of the candidate query. The candidate queries are rank ordered based on the measure of effectiveness, and one or more of the top-ranked candidate queries are presented as suggested alternatives to the user-submitted query.