Service Dispatch Adjusting Impact Factors via User Feedback
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Solution Overview
Problem
Conventional service dispatch systems fail to efficiently match user requests with suitable service suppliers, as they do not consider user behavior and preferences, leading to reduced satisfaction for both users and suppliers, and decreased system efficiency.
Innovation Solution
A method and system where a server processes user requests and adjusts service properties, such as price, based on user feedback, to calculate an impact factor for service suppliers, prioritizing requests to those most likely to accept and fulfill them efficiently, thereby improving matching accuracy and user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional service dispatch systems match users with service suppliers without considering user behavior, then the dispatching process is simple and fast, but the matching accuracy and user satisfaction decrease
Solution Approach 1:
The system implements feedback loops where user behavior data (acceptance/rejection patterns, response times, preferences) is continuously collected and fed back into the dispatching algorithm. This feedback mechanism enables the system to learn and adapt to user preferences over time, improving matching accuracy without requiring complete redesign of the dispatching architecture
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns before making dispatch decisions. By pre-processing user data to identify preferences, acceptance probabilities, and behavioral characteristics in advance, the system prepares optimized matching criteria that improve decision accuracy while keeping the real-time dispatching process efficient
2Reliability
If the system adjusts service properties based on user feedback to calculate impact factors, then user and supplier satisfaction improve, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial adjustment to service properties by focusing computational efforts on the most influential factors (e.g., price adjustments based on key user preferences) rather than comprehensively re-evaluating all possible service attributes. This selective approach maintains reliability improvement while reducing processing overhead
Solution Approach 2:
The system dynamically adjusts service parameters (such as pricing, priority levels, or service attributes) based on user feedback patterns. By changing these parameters incrementally and selectively rather than complete recalculation, the system improves matching reliability while minimizing the time and computational resources required
3Productivity
If the system prioritizes requests to service suppliers most likely to accept them, then the acceptance rate and system efficiency increase, but the complexity of predicting supplier acceptance behavior increases
Solution Approach 1:
The system enables service suppliers to indirectly 'serve' the dispatching process by their own response patterns and behavior data. By analyzing how suppliers naturally respond to different request types, locations, and conditions, the system learns acceptance probabilities without requiring complex external prediction models, thus improving efficiency while keeping the system relatively simple
Data Source
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AI summary
Various embodiments of the present disclosure relate to user behavior based service dispatch. According to embodiments, initial impact factors of a service supplier for a plurality of requests are determined. If one or more requests are associated with user feedbacks concerning an adjusted property of the service, then the related initial impact factors of the service supplier can be adjusted based on the associated user feedbacks. Based on the adjusted impact factors, one or more requests are selected from the plurality of requests to be delivered to the service supplier.