Search Ranking Adjustment via Temporal Click-Through Rate Analysis
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Solution Overview
Problem
Users face challenges in identifying relevant content items due to ambiguous queries, as search engines often return results that are not aligned with current user interests, requiring multiple query revisions to find relevant content.
Innovation Solution
The system analyzes click logs and query logs to determine historical click-through rates and identifies temporal changes in these rates, adjusting search result rankings based on these changes to disambiguate queries and provide more relevant results aligned with current user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If search engines return results based on traditional ranking methods, then the search system is simple and fast, but the results are not aligned with current user interests due to ambiguous queries
Solution Approach 1:
The system performs preliminary analysis of click logs and query logs to determine historical click-through rates before processing new queries. This pre-computed temporal information is stored and applied when disambiguating queries, allowing the system to anticipate user intent based on historical patterns without adding complexity to the real-time query processing
Solution Approach 2:
The patent introduces temporal click-through rate data as an intermediary element between the query and the ranking process. This intermediary provides contextual information about user preferences over time, enabling the system to resolve query ambiguity without fundamentally changing the core search engine architecture
2Reliability
If users revise queries multiple times to find relevant content, then the search system can return accurate results, but the user experience deteriorates and time is lost
Solution Approach 1:
The system incorporates feedback from historical click behavior into the ranking process. By analyzing what users have clicked on in the past for similar queries, the system learns user preferences and applies this feedback to improve result relevance from the first query submission, eliminating the need for iterative query revisions
Solution Approach 2:
The system performs preliminary disambiguation of queries using temporal click-through rate analysis before presenting results to users. This pre-processing step anticipates user intent based on historical patterns during specific time periods, ensuring accurate content matching without requiring users to spend time revising queries
3Adaptability or versatility
If the search system uses static ranking methods, then the system is stable and easy to maintain, but it cannot adapt to changing user interests over time
Solution Approach 1:
The patent implements dynamic ranking by computing click-through rates over different time periods and applying temporal adjustments to query disambiguation. The system adapts to changing user interests by weighing historical click data according to its recency, allowing the ranking behavior to evolve with user preferences while maintaining a relatively simple underlying architecture
Solution Approach 2:
The system changes the temporal parameter of click-through rate analysis to adapt to different time periods. By adjusting the time window and weighting of historical data, the system can respond to seasonal or trending user interests without requiring a complete restructuring of the ranking system
Data Source
AI summary
Apparatus, systems and methods for content ranking are disclosed. Historical click-through rates of first and second content items for a query are determined from an analysis of click logs and query logs. Temporal changes in the historical click-through rate of the first and second content items are identified. The rankings of the first and second content items are adjusted based on the temporal change.