Query Refinement Scoring Using Term Occurrence Analysis
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Solution Overview
Problem
Users often face difficulties in formulating queries that accurately represent their information needs, leading to ambiguous, broad, or specific queries that yield low-quality search results, especially when unfamiliar with the topic or language of the resources being searched, resulting in a time-consuming refinement process.
Innovation Solution
A method is developed to identify candidate query refinements by generating term occurrence scores and query refinement scores from past user queries, which are then used to suggest refinements, enabling efficient refinement of search queries by storing data associating these scores with candidate query refinements for selection and display to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users formulate queries manually without assistance, then query accuracy depends on user knowledge, but users unfamiliar with the topic or language produce ambiguous or broad queries that yield low-quality results
Solution Approach 1:
The system performs preliminary analysis of past query refinements and pre-calculates term occurrence scores and query refinement scores before the user needs them. When a user submits a query, the system has already prepared candidate refinements and their associated scores, enabling immediate suggestion without requiring the user to manually explore query variations.
Solution Approach 2:
The system analyzes historical feedback from past user queries and refinements to generate term occurrence scores. By incorporating this feedback loop, the system learns from actual user refinement behavior and uses it to suggest query improvements that have proven effective in previous search sessions, thereby improving query accuracy based on empirical evidence.
2Measurement precision
If users enter multiple different queries to refine their search, then search result quality improves, but the time required to find relevant information increases
Solution Approach 1:
The system pre-calculates query refinement scores for multiple candidate refinements before the user needs them. By having these scores ready in advance based on historical data, the system can immediately present the most promising refinements when a user submits a query, eliminating the time-consuming trial-and-error process of manually testing different query variations.
Solution Approach 2:
The system copies successful query refinement patterns from historical data and applies them to current queries. By identifying and replicating effective refinement strategies from past user behavior, the system provides pre-tested query suggestions that have proven to lead to relevant search results, reducing the need for users to independently discover these refinements.
3Adaptability or versatility
If the system provides many candidate query refinements, then users have more options for accurate queries, but the complexity of selecting the best refinement increases
Solution Approach 1:
The system transforms the complex multi-dimensional problem of query refinement selection into a simpler ranking problem by computing a single query refinement score for each candidate. This score aggregates multiple factors (term occurrence frequency, refinement effectiveness, etc.) into one parameter that directly indicates the quality of each refinement option, making it easy for users to compare and select the best candidate.
Solution Approach 2:
The query refinement score acts as an intermediary metric that mediates between the complex historical data and the user's selection decision. Instead of presenting raw historical statistics or requiring users to evaluate multiple complex factors, the system uses this intermediate score to translate complex patterns into a simple, actionable ranking that guides user selection.
4Measurement precision
If the system calculates detailed term occurrence scores and query refinement scores, then query suggestion accuracy improves, but the computational processing required increases
Solution Approach 1:
The system performs the computationally intensive score calculations in advance, before users submit their queries. By pre-computing term occurrence scores and query refinement scores from historical data and storing them for rapid retrieval, the system shifts the computational burden to offline processing, enabling fast, accurate suggestions when users need them without requiring heavy real-time processing power.
Data Source
AI summary
In one implementation, a method is described for identifying candidate query refinements for a given query from past queries submitted by one or more users. The method includes generating from the candidate query refinements, term occurrence scores for terms in the candidate query refinements indicating frequency of the terms within the candidate query refinements. The method further includes calculating query refinement scores for the candidate query refinements of the given query using the term occurrence scores. The method further includes storing data associating the query refinement scores with the candidate query refinements for use in selecting from among the candidate query refinements for the given query.