Edge-Cloud Recommendation Model Selection for Accuracy and Latency
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Solution Overview
Problem
Existing recommendation systems face challenges in balancing real-time efficiency and accuracy, with cloud-based systems consuming high network resources and mobile device-based systems lacking in real-time interaction capabilities, leading to suboptimal recommendation effects.
Innovation Solution
A system and method for edge-cloud collaborative recommendation that selects a matched recommendation model from both end-side and cloud-side models based on a relative recommendation matching degree, utilizing a controller trained on a model selection data set to improve recommendation efficiency and accuracy by leveraging the strengths of both environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud-side recommendation model is used, then recommendation accuracy is improved, but network resource consumption increases
Solution Approach 1:
The recommendation system is segmented into cloud-side and end-side components. The cloud server provides comprehensive recommendation models for high accuracy, while the end device runs lightweight models for low-resource scenarios. This segmentation allows the system to distribute computational tasks appropriately, reducing network resource consumption while maintaining recommendation accuracy.
Solution Approach 2:
The system dynamically selects between cloud-side and end-side recommendation models based on real-time conditions such as network status, user behavior patterns, and device capabilities. This dynamic adaptation enables the system to use cloud-side models when network resources are abundant and switch to end-side models when network resources are constrained, thereby optimizing the balance between recommendation accuracy and network resource consumption.
2Use of energy by moving object
If end-side recommendation model is used, then network resource consumption is reduced, but recommendation accuracy decreases
Solution Approach 1:
The recommendation functionality is segmented into lightweight end-side models and comprehensive cloud-side models. End-side models are optimized for low resource consumption and can operate independently when network resources are limited, while cloud-side models provide enhanced accuracy when resources are abundant. This segmentation ensures that reduced network resource consumption does not permanently sacrifice recommendation accuracy.
Solution Approach 2:
The system dynamically adjusts between end-side and cloud-side models based on network conditions, device state, and user interaction patterns. When network resources are plentiful and user engagement is high, the system transitions to cloud-side models to maximize recommendation accuracy. When network resources are constrained, it switches to end-side models, thereby adaptively maintaining optimal performance under varying conditions.
3Reliability
If cloud-based recommendation system is used, then recommendation reliability is improved, but real-time interaction capability deteriorates
Solution Approach 1:
The recommendation system is divided into cloud-based and end-based components. The end device executes lightweight recommendation algorithms locally to provide immediate real-time interactions, while the cloud server runs comprehensive models to ensure recommendation reliability. This segmentation enables the system to deliver both real-time responsiveness and reliable recommendations by utilizing the appropriate component based on the specific task requirements.
Solution Approach 2:
The system dynamically switches between cloud-based and end-based recommendation processing based on real-time network conditions, user behavior patterns, and interaction urgency. For time-sensitive interactions, the system prioritizes end-based processing to maintain real-time capability. For scenarios requiring high reliability and comprehensive analysis, it transitions to cloud-based processing, thereby dynamically balancing real-time interaction capability with recommendation reliability.
4Speed
If mobile device-based recommendation system is used, then real-time interaction capability is improved, but recommendation reliability decreases
Solution Approach 1:
The recommendation system is segmented into end-device lightweight models and cloud-based comprehensive models. End-device models provide fast real-time interaction capability, while cloud-based models ensure recommendation reliability through more sophisticated algorithms and access to extensive data. This segmentation allows the system to leverage the speed of end-device processing while compensating for reliability limitations through cloud-based validation and enhancement.
Solution Approach 2:
The system dynamically adjusts between end-device and cloud-based recommendation processing based on network availability, interaction context, and performance requirements. When real-time interaction is critical and network conditions permit, the system uses end-device models for immediate responses. When recommendation reliability is paramount and resources are available, it transitions to cloud-based models, thereby dynamically optimizing the balance between real-time interaction capability and recommendation reliability.
Data Source
AI summary
Embodiments of the present disclosure provide a system and method for edge-cloud collaborative recommendation, and an electronic device. The system for edge-cloud collaborative recommendation includes: a terminal device and a cloud server. The cloud server is arranged for: obtaining user feature data of the terminal device; selecting, based on a relative recommendation matching degree between multiple recommendation models and the user feature data, a matched recommendation model from the multiple recommendation models, the multiple recommendation models including an end-side recommendation model deployed in the terminal device and a cloud-side recommendation model deployed in the cloud server, and the relative recommendation matching degree indicating a relative recommendation effect of the multiple recommendation models on the user feature data; and recommending to the terminal device based on the matched recommendation model. According to the solution of the embodiments of the present disclosure, efficient collaboration between the end side recommendation model and the cloud side recommendation model can be implemented, and the recommendation effect can be improved.