Hybrid Information Recommendation Using Dynamic Weight Coefficients
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Solution Overview
Problem
Existing weight-coefficient-based hybrid recommendation methods require manual adjustment of weight coefficients, leading to low accuracy and increased network resource consumption and server pressure due to the inability to timely adapt to changing user preferences.
Innovation Solution
An automated method that determines historical user behavioral information, adjusts weight coefficients using optimization algorithms, and iteratively refines the recommendation list until it meets predetermined conditions, eliminating the need for manual coefficient setting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If weight coefficients are manually set and adjusted based on observation or heuristic experience, then the recommendation system can be configured, but it requires a lot of time and labor and has low accuracy
Solution Approach 1:
The system automatically adjusts weight coefficients based on user behavioral data without requiring manual configuration. The optimization algorithm self-tunes the weights by analyzing user interactions, clicking patterns, and preference evolution, thereby achieving both ease of operation and high accuracy simultaneously
Solution Approach 2:
The weight coefficients are dynamically changed based on real-time user behavioral patterns rather than being fixed. The system continuously updates parameters according to observed user actions, allowing the recommendation system to adapt to changing preferences while maintaining high accuracy
2Ease of manufacture
If weight coefficients are set based on heuristic experience, then the recommendation system can be implemented, but it cannot timely adjust when user preference changes
Solution Approach 1:
The weight coefficients transition from static values based on heuristic experience to dynamic values that continuously adapt to user preferences. The optimization algorithm processes new user behavioral data in real-time, allowing the system to respond promptly to changing preferences while maintaining implementation simplicity
Solution Approach 2:
The system incorporates user behavioral feedback into the weight coefficient adjustment process. By continuously monitoring user interactions and using this feedback to refine weights through optimization algorithms, the system achieves timely adaptation to preference changes while remaining easy to implement
3Productivity
If manually adjusted weight coefficients are used, then the recommendation list can be generated, but user search time increases and network resources are consumed
Solution Approach 1:
The system automatically generates optimized recommendation lists without requiring manual weight adjustment, thereby reducing the time users need to spend searching. The self-service optimization process continuously refines recommendations based on user behavior, improving information retrieval productivity while minimizing user search time and network resource consumption
Data Source
AI summary
Historical behavioral information of a user is retrieved, where the historical behavioral data includes data associated to operations performed by the user on a server. Recommended information sets are determined based on the historical behavioral information. A plurality of weight coefficients are generated for the plurality of recommended information sets. A recommendation list is determined based on the plurality of weight coefficients. It is determined whether the recommendation list satisfies a recommendation condition. If the recommendation list satisfies the recommendation condition, a recommendation based on the recommendation list is transmitted to the user device.