Search Result Removal Through Attribute-Based User Filtering
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Solution Overview
Problem
Existing search technologies fail to adequately exclude or remove particular search results for a given query, leading to user experience issues, inaccurate search results, and excessive computing resource consumption.
Innovation Solution
Implement a system that automatically marks and removes search results based on user input, using machine learning models to determine item listing attributes, categories, and similarities, allowing users to specify exclusion criteria through a single UI element.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing search technologies use traditional algorithms to rank and retrieve search results, then search results are provided to users, but the system cannot adequately exclude unwanted search results and consumes excessive computing resources
Solution Approach 1:
The system pre-computes and stores item listing attributes, categories, and similarity relationships in databases before search queries are executed. This preliminary action enables the search engine to quickly filter and exclude unwanted results using pre-established criteria without performing complex computations during actual search operations, thereby reducing real-time computing resource consumption while maintaining high search result accuracy
Solution Approach 2:
The system extracts and separates the exclusion function from traditional search algorithms by implementing a dedicated mechanism that identifies and removes unwanted search results based on user-specified criteria. This extraction allows the system to efficiently eliminate irrelevant results (such as items from unwanted categories or similar to previously viewed items) without burdening the main search computation, thus improving reliability while reducing energy consumption
2Reliability
If users manually review and select search results one by one, then accurate search results can be obtained, but user experience deteriorates and time consumption increases
Solution Approach 1:
The system automatically performs the exclusion of unwanted search results by leveraging pre-stored item attributes, categories, and similarity data. When a user specifies exclusion criteria (such as excluding items from certain categories or items similar to a selected item), the system self-service filters and removes matching results without requiring manual user intervention for each item, thereby maintaining high accuracy while significantly reducing the time users would otherwise spend reviewing results
Solution Approach 2:
The system pre-establishes item listing attributes, categories, and similarity relationships in databases before users need to search. This preliminary preparation enables rapid automatic filtering when users provide exclusion criteria, eliminating the need for users to manually evaluate each search result and thus reducing time loss while preserving search accuracy
3Ease of operation
If the system removes multiple search results based on a single user selection, then user experience improves and computing resources are saved, but the system complexity increases
Solution Approach 1:
The system pre-computes and stores item attributes, categories, and similarity relationships in structured databases before runtime. This preliminary action simplifies the complexity by moving computational burden from the interaction phase to the preparation phase, allowing the user interface to operate simply while the backend efficiently handles complex filtering operations based on pre-established data
Solution Approach 2:
The system introduces an intermediary layer consisting of pre-stored item attributes, categories, and similarity data that mediates between user selections and search result filtering. This intermediary structure simplifies the user interface by providing clear exclusion options while managing system complexity through organized, pre-processed data relationships that enable efficient batch removal of unwanted results
Data Source
AI summary
Computer information retrieval by automatically marking at least a first item listing, of a first set of item listings, as a candidate for removal as a search result for a query. Such automatic marking occurs in response to receiving an indication that a selection has been made at a computing device, where the selection is at least partially indicative of the user requesting removal, from a set of search results, of a first item listing based on a particular attribute value associated with the first item listing.