User-Adjustable Recommendation Parameters for Personalization
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Solution Overview
Problem
Current intelligent recommendation systems lack user participation and personalization, relying on large sample data and fixed user profiles, leading to non-updated recommendations that fail to adapt to changing user preferences and privacy concerns.
Innovation Solution
A user-adjustable intelligent recommendation method is introduced, where a user interface allows users to modify recommendation parameters such as feature values and weights, enabling participation in the recommendation process and providing more accurate and personalized content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If recommendation is performed based on fixed user profile and large sample data, then recommendation coverage is improved, but personalization and user satisfaction deteriorate
Solution Approach 1:
The patent implements dynamic user profiles that can be adjusted in real-time through user interaction. The system transitions from static, fixed user profiles to dynamic profiles that adapt as users modify their preferences, allowing the recommendation system to balance coverage and personalization by updating profiles based on user feedback and behavior changes.
Solution Approach 2:
The patent introduces feedback mechanisms where users can directly interact with and modify their profile parameters. This feedback loop allows users to correct unexpected recommendations by adjusting their profile settings, enabling the system to learn from user corrections and improve both personalization accuracy and overall recommendation quality.
2Adaptability or versatility
If recommendation model is updated in real-time based on user behavior, then personalization is improved, but system complexity and computational resources deteriorate
Solution Approach 1:
The patent pre-establishes the framework for user profile adjustment and real-time updating capabilities during system design. By preparing the infrastructure in advance for handling user feedback and profile modifications, the system can achieve real-time adaptation without requiring complex computational resources during actual recommendation generation, as the basic update mechanisms are already in place.
3Ease of operation
If user profile parameters are made adjustable by users, then user participation and personalization are improved, but system operation complexity deteriorates
Solution Approach 1:
The patent enables users to directly manage and adjust their own profile parameters through an intuitive interface. Users can independently modify their preferences, correct unexpected recommendations, and control their own personalization experience without requiring complex system operations or administrator intervention, thereby simplifying system operation while enhancing user participation.
Data Source
AI summary
The present application relates to an intelligent recommendation method, a terminal, and a server. The method includes: displaying a first modification interface in response to a first operation that is input by a user. The first modification interface displays a plurality of recommendation parameters. The method further comprises sending a first modification request to a first server in response to a second operation that is input by the user. The first modification request requests the server to modify at least one recommendation parameter. The method further comprises sending a recommendation request to the server, receiving a recommended content from the server and displaying the recommended content.