Federated Search Ranking with Feedback-Driven Personalization
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Solution Overview
Problem
Existing search engines struggle to adapt to individual user preferences and provide personalized and relevant search results over time, lacking effective mechanisms to incorporate user feedback for iterative improvement.
Innovation Solution
Implementing a federated learning-based system that utilizes reinforcement learning to refine search results by incorporating user feedback, both individually and collectively across multiple users, through the use of vector representations and embedding spaces to adjust similarity scores based on positive and negative feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional search engines use static search algorithms, then search results are consistent and reliable, but they cannot adapt to individual user preferences and provide personalized results
Solution Approach 1:
The search engine transitions from static search algorithms to dynamic reinforcement learning models that continuously adapt to user preferences. The system learns from user feedback (clicks, views, dwell time) and updates search results in real-time, making the search algorithm flexible and responsive to individual user needs while maintaining reliability through data-driven optimization.
Solution Approach 2:
The patent implements feedback mechanisms where user interactions with search results are captured and used to train reinforcement learning models. This feedback loop allows the system to learn from user behavior patterns and continuously improve search relevance, resolving the contradiction between adaptability and reliability by using feedback to refine consistent results over time.
2Manufacturing precision
If search engines incorporate user feedback for iterative improvement, then search result relevance improves over time, but system complexity increases
Solution Approach 1:
The search engine performs self-service learning through reinforcement learning models that automatically optimize search results based on user feedback without requiring manual retraining or complex external intervention. The system self-adjusts search algorithms based on observed user behavior, improving precision while managing complexity through automated decision-making processes.
Solution Approach 2:
The patent changes key parameters of the search system by transitioning from traditional ranking algorithms to reinforcement learning frameworks that optimize based on user feedback parameters. This parameter transformation enables iterative improvement of search precision while the underlying mathematical frameworks manage system complexity through standardized optimization techniques.
3Manufacturing precision
If search engines provide personalized results based on individual user feedback, then search relevance increases, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary learning and modeling of user preferences during off-peak times or in the background, building up reinforcement learning models before actual search queries are processed. This preliminary action allows the system to prepare personalized search strategies in advance, reducing real-time processing requirements while maintaining high relevance.
Solution Approach 2:
The patent applies partial reinforcement learning updates rather than complete retraining for each search query. The system uses incremental learning where only necessary model parameters are updated based on new feedback, providing personalized results without the computational burden of full system reprocessing, thus balancing relevance with processing time.
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 plurality of first users at a computer system, a query for a product, and generate a vector representation of the query. The system can then execute a search of an embedding space for the vector representation, resulting in a first ranked list of at least one product, wherein the embedding space comprises vectorized product representations for a plurality of products, and wherein the first ranked list is ranked in order of similarity of the vectorized product representations to the vector representation of the query. As feedback is received from multiple users, and the query is received again, the system can generate a second ranked list which is the first ranked list modified by the feedback of the plurality of first users.