Search Apparatus Item Category Evaluation for Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current search systems face challenges in efficiently refining search results when users lack specific keywords, leading to unnecessary work and inefficiency, as existing suggestion features primarily offer individual keywords rather than evaluating the usefulness of entire item categories.
Innovation Solution
A search apparatus calculates document counts based on logical AND operations of keywords within items and evaluates item usefulness by averaging refinement effects, providing recommendation information to guide users in selecting relevant items for refining search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the user enters more keywords to narrow down search results, then the precision of search results is improved, but the time required for search is increased
Solution Approach 1:
The system performs preliminary analysis of the search query to automatically identify and extract candidate keywords that should be added. By pre-calculating relevant keywords and their potential impact on search results, the system prepares refinement suggestions before the user needs them, thus reducing the time required for iterative search refinement while maintaining precision.
Solution Approach 2:
The system provides feedback to the user by presenting candidate keywords with metadata (such as expected document count reduction) that helps the user understand the potential impact of adding each keyword. This feedback mechanism enables the user to make informed decisions about which keywords to add, optimizing the balance between search precision and time investment.
2Ease of operation
If the system provides individual keyword suggestions, then the user can refine search results, but the user may perform futile work by inspecting items that do not yield effective refinement
Solution Approach 1:
The system performs preliminary evaluation of each candidate keyword's potential effectiveness by estimating the document count reduction that would result from adding the keyword. This pre-assessment allows the system to filter out low-value keywords before presenting them to the user, preventing futile inspection work while maintaining ease of operation.
Solution Approach 2:
The system changes the presentation parameters of keyword suggestions by including metadata such as expected document count reduction or refinement effectiveness scores. This additional information helps users prioritize which keywords to inspect first, reducing the time spent on futile inspection while maintaining ease of operation.
3Ease of operation
If the user starts with a small number of keywords and adds keywords gradually, then the search process becomes manageable, but the time taken to refine search results increases
Solution Approach 1:
The system performs preliminary analysis to identify multiple candidate keywords that could be added to the current search query. By pre-processing the query and generating a ranked list of candidate keywords with effectiveness metrics, the system enables the user to efficiently progress through refinement steps without manual analysis, thus improving productivity while maintaining manageability.
Solution Approach 2:
The system provides feedback in the form of ranked candidate keywords with metadata indicating expected refinement effectiveness. This feedback guides the user's keyword selection process, enabling faster convergence to precise search results while keeping the process manageable through clear prioritization suggestions.
4Reliability
If the system evaluates and recommends entire item categories instead of individual keywords, then the usefulness of recommendations is improved, but the complexity of the system increases
Solution Approach 1:
The system merges multiple individual keyword evaluations into a unified item category evaluation. By aggregating the potential effectiveness of individual keywords within an item category, the system provides higher-level recommendations that are more useful to users while managing complexity through consolidation rather than evaluating every individual keyword in isolation.
Solution Approach 2:
The system creates a multi-functional evaluation mechanism that can assess both individual keywords and entire item categories using a unified framework. This universal evaluation approach improves recommendation usefulness by providing flexible, context-appropriate suggestions while avoiding the need for separate complex evaluation systems for different recommendation granularities.
Data Source
AI summary
A control unit calculates a first document count indicating the number of documents that are retrieved based on a first keyword, a second document count indicating the number of documents that are retrieved based on a logical AND of the first keyword and a second keyword belonging to one item, and a third document count indicating the number of documents that are retrieved based on a logical AND of the first keyword and a third keyword belonging to the one item, from the set of documents. The control unit calculates an evaluation value of the one item, based on the first, second, and third document counts. When the evaluation value satisfies a predetermined condition, the control unit outputs recommendation information recommending use of the one item to narrow down documents that are retrieved.