Federated Learning Search Ranking for Adaptive User Preferences
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Solution Overview
Problem
Existing search engines struggle to adapt to user preferences and expectations over time, leading to suboptimal search results despite user feedback.
Innovation Solution
A federated learning-based search engine system that utilizes user feedback to refine search results through reinforcement learning, modifying similarity scores in an embedding space to improve query relevance over iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a search engine uses static similarity scoring based on initial query embeddings, then the search system maintains computational efficiency and simplicity, but the search results fail to adapt to user preferences and expectations over time
Solution Approach 1:
The patent implements feedback loops where user interactions with search results (clicks, dwell time, re-rankings) are continuously collected and used to update the reinforcement learning model. This feedback mechanism enables the system to learn and adapt to user preferences over time, resolving the contradiction between adaptability and complexity by making the system progressively smarter through data-driven learning rather than requiring complex pre-programmed rules
Solution Approach 2:
The search engine transitions from static similarity scoring to dynamic ranking that adapts based on user behavior patterns. The reinforcement learning model continuously updates the ranking function to reflect changing user preferences, making the system dynamic rather than static. This allows the same query to produce different rankings based on accumulated user feedback, achieving adaptability without proportionally increasing complexity
2Measurement precision
If a search engine collects and processes user feedback to improve search results, then search relevancy improves over time, but the time required to process feedback and generate updated rankings increases
Solution Approach 1:
The system performs preliminary actions by pre-processing user feedback and updating the reinforcement learning model in background processes rather than waiting for explicit user requests. The model learns from aggregated feedback patterns over time, so when a user submits a query, the system can quickly apply pre-computed ranking adjustments without time-consuming real-time analysis of individual feedback instances
Solution Approach 2:
The patent applies partial action by selectively updating only the portions of the ranking model that are most impacted by user feedback, rather than re-computing entire ranking structures. The reinforcement learning approach focuses computational resources on learning the most significant preference patterns, achieving improved accuracy without proportionally increasing processing time for all search operations
3Reliability
If a search engine uses reinforcement learning to modify rankings based on user feedback, then user-specific search quality improves, but the computational resources required to train and update the model increase
Solution Approach 1:
The patent segments the learning process into distributed components that can operate independently. The reinforcement learning model is trained on distributed data from multiple users across different systems, with each contribution being processed incrementally. This segmentation allows the computational burden to be distributed across multiple processing units and time periods, reducing peak energy consumption while maintaining model reliability through aggregated learning
Solution Approach 2:
The system implements self-service by automatically updating its ranking model using feedback from normal user interactions without requiring separate training campaigns. The reinforcement learning architecture is designed to learn continuously from operational data, making the system self-improving without additional computational overhead beyond normal search operations. This eliminates the need for separate, energy-intensive training phases
Data Source
AI summary
Systems, methods, and computer-readable storage media for reinforcing and modifying the search engine behavior using aspects of federated learning. A system can receive from a user at a first time, a query for a product, then generate a vector representation of the query, and execute a search of an embedding space for the vector representation, resulting in at least one similarity score for at least one product. The system can present to the user search results based on the at least one similarity score, and receive from the user a selected product from within the search results. The system can update, based on the selected product, the at least one similarity score for the selected product, resulting in at least one updated similarity score, where when the query is received again the search of the embedding space for the vector representation produces the at least one updated similarity score.