Context-Sensitive Search Ranking via Click Models
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Solution Overview
Problem
Conventional search engines struggle to provide relevant search results as they often return a broad spectrum of content, even when a user is searching for specific context, leading to irrelevant information being prioritized over relevant results.
Innovation Solution
The implementation of a context-sensitive ranking system that identifies user context through selected content during a session, using contextual click models to weight search results based on past user actions, thereby refining search results to match the user's intended context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional search engines return a broad spectrum of content for a search query, then the search engine provides comprehensive coverage of possible topics, but the search results include too much irrelevant content and fail to focus on the user's specific context
Solution Approach 1:
The patent segments the broad search results into multiple context-specific groups (e.g., shopping context, news context, definition context). Each segment is processed and ranked separately using context-appropriate click models, allowing the system to maintain comprehensive coverage while presenting focused results for each context category to the user.
Solution Approach 2:
The patent applies different ranking strategies (local quality) to different context segments. Instead of using a single uniform ranking approach for all results, the system tailors the ranking methodology to each specific context type, using context-specific click models that are optimized for that particular domain or user intent.
2Measurement precision
If click models are used to modify search result ranking based on user-selected results, then the search results become more focused on popular contexts, but the most popular context is not necessarily the context in which the user is actually interested
Solution Approach 1:
The patent implements dynamic context detection that adapts to the user's current session and behavior patterns. Rather than relying solely on historical popularity data, the system dynamically identifies the user's current context based on real-time interactions, allowing the ranking to shift flexibly to match the user's actual interests rather than just popular topics.
Solution Approach 2:
The system uses feedback from user interactions during the current session to continuously refine and update the detected context. By monitoring user selections, clicks, and engagement patterns in real-time, the system adjusts the context identification and ranking strategy to better align with the user's evolving interests throughout the search session.
Data Source
AI summary
Methods, systems, and apparatus, including computer program products, in which context can be used to rank search results. Context associated with a user session can be identified. A search query received during the user session can be used to identify a contextual click model based upon the context associated with the user session.