Query-Specific Search Ranking Using Click-Through Selection Data
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Solution Overview
Problem
Users face difficulty in locating specific video content items or providers due to the large quantity of available options, leading to inefficient search results and user frustration when relying solely on global popularity rankings.
Innovation Solution
Implement a query-specific ranking technique using machine learning to rank search results based on prior click-through data associated with user queries, prioritizing content entities that have been frequently selected for output in the context of those queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If global popularity rankings are used to rank search results, then the system is simple to implement, but the relevance to user intent deteriorates
Solution Approach 1:
The patent changes the ranking parameters from simple global popularity metrics to query-specific relevance metrics. The machine learning model dynamically adjusts ranking parameters based on the specific query context, user behavior patterns, and relationship between queries, thereby improving relevance to user intent while maintaining system implementability through automated learning.
Solution Approach 2:
The patent replaces the mechanical sorting system based on global popularity with an intelligent machine learning system. This substitution enables the system to automatically learn and adapt ranking criteria based on query relationships and user behavior, significantly improving relevance measurement without requiring manual configuration of complex ranking rules.
2Adaptability or versatility
If the quantity of video content items and providers increases, then the system offers more content variety, but the difficulty of locating specific content increases
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously learns from user interactions and query patterns. The system uses feedback from click-through data, user selections, and query relationships to refine ranking accuracy, enabling users to quickly locate specific content even as the overall content library expands and diversifies.
Solution Approach 2:
The patent performs preliminary ranking and filtering actions before presenting results to users. The machine learning model pre-processes and ranks content based on query-specific relevance and predicted user intent, so that when users search, the most relevant results are already positioned at the top, reducing the time and effort needed to locate specific content amidst large quantities of available content.
3Measurement precision
If query-specific ranking using machine learning is implemented, then the relevance to user intent improves, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by focusing the machine learning model on ranking only the top candidate results rather than processing all possible content items. This approach achieves high relevance to user intent for the most important results while limiting computational complexity by not requiring exhaustive analysis of the entire content library.
Solution Approach 2:
The system performs preliminary filtering and candidate selection before applying the computationally intensive machine learning ranking. By pre-processing the content to identify relevant candidates based on basic query matching, the system reduces the input size for the machine learning model, thereby maintaining high relevance while managing computational complexity.
Data Source
AI summary
Systems, methods, and apparatuses for query-specific ranking of search results are described herein. A first query may be received from a user device. A set of candidate results associated with the first query may be determined. The set of candidate results may be ranked based on inputting the set of candidate results in a machine learning model. The machine learning model may be trained using data indicating previously selected candidate results output in response to the first query and previously selected candidate results output in response to a second query. The second query may comprise the first query. Output, via the user device, of an indication of the ranked candidate results may be caused.