Dynamic Ground Truth Table for Search Relevance
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Solution Overview
Problem
Current search systems fail to optimize search results efficiently due to inefficient organization and inaccuracy, leading to wasted time and screen space as users navigate through irrelevant information.
Innovation Solution
A ground truth table is modified based on real-time user interactions such as click-through, purchase, wish list addition, and share actions, adjusting the relevance ranking of search results to provide improved results for subsequent queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If search results are organized using a static ground truth table, then the system structure is simple and easy to maintain, but the search result relevance and accuracy deteriorate over time due to lack of adaptation to user behavior
Solution Approach 1:
The ground truth table is transformed from a static data structure to a dynamic one that automatically updates based on user interactions. The system continuously modifies the ground truth table by incorporating user behavior data (clicks, purchases, shares) to adjust relevance rankings, enabling the search system to adapt to changing user preferences and improve result relevance over time without requiring manual intervention
Solution Approach 2:
The system implements a feedback loop where user interactions with search results are captured and used to modify the ground truth table. User behavior data flows back into the system to continuously refine relevance rankings, creating a self-improving mechanism that enhances search quality based on actual user preferences rather than static predefined criteria
2Measurement precision
If the ground truth table is modified based on real-time user interactions, then search result accuracy improves over time, but the data processing complexity and computational resources increase
Solution Approach 1:
The ground truth table performs self-updates by automatically incorporating user interaction data without requiring external manual intervention. The system autonomously processes user behavior signals, calculates relevance adjustments, and modifies the ground truth table structure itself, reducing the need for complex external processing systems while maintaining high accuracy
Solution Approach 2:
The system pre-processes and structures user interaction data as it is collected, organizing it in a manner that facilitates efficient subsequent processing. By preparing the data in advance and establishing clear update rules for the ground truth table, the system reduces the computational burden during real-time search operations and minimizes processing complexity
3Adaptability or versatility
If user interactions are recorded and used to modify search results, then the system becomes more adaptive to user preferences, but the time required to process and update search data increases
Solution Approach 1:
The system implements periodic updates to the ground truth table rather than continuous real-time modifications. User interactions are accumulated and processed at scheduled intervals, allowing the system to maintain high adaptability to user preferences while avoiding the performance overhead of constant updates. This periodic approach balances adaptability with efficient resource utilization and minimal impact on search response time
Data Source
AI summary
In embodiments, a ground truth table is used to help determine the relevance of a set of search results from a search query. The ground truth table comprises numerous items, along with a corresponding relevancy ranking for each item. Based on the user interaction with the set of search results, the ground truth table is modified, such that subsequent search queries provide improved results over time. The user interactions can include a click-through action, in which case a user clicked on (or otherwise selected) a search result entry. The user interactions can include a purchase action, wish list addition action, favorite list addition action, and/or a share action, among others. Based on the user interactions, the ranking of an entry in the ground truth table is adjusted. Then, subsequent searches that use the modified ground truth table can provide improved search results.