Query Modality Recommendation for Search Engines
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Solution Overview
Problem
Existing search technologies often provide search results that are too vast and broad, requiring users to submit additional queries or apply multiple filters to find specific items, leading to unnecessary consumption of computing resources and user frustration.
Innovation Solution
A search engine that recommends a different query modality based on the categories associated with search results, allowing users to switch from one modality (e.g., text-based or image-based) to another that better matches their intent, thereby improving the relevance of search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If search results are provided using the same query modality (text or image), then the search system maintains simplicity in processing, but the search results become too vast and broad, requiring additional filters and queries
Solution Approach 1:
The patent introduces a recommendation module as an intermediary between the query processing module and the user. This module analyzes search results and recommends alternative query modalities without requiring the user to manually apply filters or submit multiple queries, thus improving result relevance while maintaining processing simplicity
Solution Approach 2:
The system performs preliminary analysis of search results to determine the optimal query modality before the user encounters broad or irrelevant results. By proactively recommending the best modality based on result categories and user behavior patterns, the system prevents the need for additional filtering operations
2Measurement precision
If users submit multiple queries and apply filters to find specific items, then the precision of search results improves, but computing resource consumption increases
Solution Approach 1:
The patent implements a feedback mechanism where the recommendation module continuously monitors user interactions with search results and adjusts modality recommendations accordingly. By learning from user behavior patterns and result engagement metrics, the system refines its recommendations to achieve higher precision with fewer queries, thereby reducing computing resource consumption
Solution Approach 2:
The system dynamically changes the query modality parameter based on analyzed result categories and user preferences. Instead of repeatedly processing queries in the same modality with increasing filter complexity, the system switches modalities (e.g., from text to image-based search) to achieve precise results more efficiently
3Reliability
If users repeatedly submit queries and select filters, then the accuracy of finding specific items improves, but user frustration increases and time is wasted
Solution Approach 1:
The recommendation module acts as an intermediary that anticipates user needs by analyzing search result patterns and proactively suggesting optimal query modalities. This eliminates the need for users to manually iterate through multiple queries and filter selections, significantly reducing search time while maintaining high item finding accuracy
Solution Approach 2:
The system performs preliminary analysis of search results and user behavior to determine the most effective query modality before the user experiences frustration from broad or irrelevant results. By preparing and recommending the optimal approach in advance, the system prevents time-wasting iterative searching
Data Source
AI summary
A query modality recommendation system provides recommendations to use a particular query modality based on one or more categories of search results for a search query. Upon receiving a search query in a first query modality at a search engine, the query modality recommendation system determines to recommend use of a second query modality based on one or more categories of the search results. For example, the first query modality may be a textual query and the second query modality may be an image query. In aspects, recommending use of the second query modality comprises comparing a first search performance of the one or more categories for the first query modality in historical search queries to a second search performance of the one or more categories for the second query modality in the historical search queries.