AI Call Recommendation System for Dynamic Pricing and Matching
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Solution Overview
Problem
Transport service providers face difficulties in selecting the most suitable service requests from multiple requests due to the lack of a systematic approach to match preferences and dynamic pricing based on real-time data.
Innovation Solution
A call recommendation system utilizing artificial intelligence, specifically a combination of recurrent neural networks (RNN) and deep learning algorithms, to predict future locations of service users and providers, determine matching times, and dynamically set prices based on these predictions, thereby ranking service requests according to provider preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple service requests are received simultaneously, then the service provider can serve more users, but it becomes difficult to select the most suitable service request according to provider preference
Solution Approach 1:
The system automatically ranks and recommends service requests based on provider preferences without requiring manual evaluation by the provider. The AI system performs the selection task autonomously by analyzing multiple requests against stored preference data and generating a ranked list, allowing the provider to simply choose from pre-ranked options rather than manually evaluating each request.
Solution Approach 2:
The manual selection process is replaced with an AI-based automated ranking system. Instead of the provider manually reviewing and comparing service requests, the system uses machine learning models to automatically evaluate and rank requests based on provider preferences, substituting human cognitive effort with computational processing.
2Reliability
If manual selection of service requests is used, then provider preferences can be considered, but the matching process becomes time-consuming and inefficient
Solution Approach 1:
The system pre-ranks service requests based on provider preferences before the provider needs to make a selection. By performing the evaluation and ranking in advance using AI algorithms, the system prepares a ready-to-use ranked list, eliminating the time the provider would spend on manual evaluation while ensuring preferences are accurately considered.
Solution Approach 2:
The time-consuming manual evaluation process is replaced with rapid AI-based automated ranking. The system uses pre-trained machine learning models to instantly evaluate and rank service requests according to stored provider preferences, reducing selection time from minutes or hours to seconds while maintaining or improving matching quality.
3Measurement precision
If dynamic pricing based on real-time data is implemented, then pricing accuracy improves, but system complexity increases
Solution Approach 1:
The AI system performs multiple functions using a unified framework: it predicts future locations, estimates matching times, and determines dynamic prices all through integrated machine learning models. This multi-functional approach achieves high pricing accuracy by considering multiple factors simultaneously while avoiding the complexity of separate specialized systems for each function.
Solution Approach 2:
The system dynamically adjusts pricing based on changing parameters such as predicted matching time, future locations, and real-time conditions. By using machine learning models that automatically adapt to parameter changes, the system achieves high pricing precision without requiring complex manual adjustment mechanisms, as the models learn optimal pricing strategies from historical data.
Data Source
AI summary
A call recommendation system based on artificial intelligence is provided. The call recommendation system includes a data collecting unit, a matching time predicting unit, a price determining unit, and a final ranking determining unit. When a service is requested from a service user, the data collecting unit collects first past data indicating a past location of the service user, first present data indicating a present location of the service user, second past data indicating a past location of a service provider, and second present data indicating a present location of the service provider. The matching time predicting unit inputs the first and second past data and the first and second present data to a recurrent neutral network (RNN) leaning model to predict a future location of the service user and a future location of the service provider and inputs first prediction data regarding the future location of the service user and second prediction data regarding the future location of the service provider to a prediction learning model to predict, when the service provider selects a service, a matching time required until the service provider is matched with a next service user after the service provider completes the service. The price determining unit determines a price for the service such that the price increases as the matching time increases. The final ranking determining unit determines a recommendation rating (or a recommendation priority) of a service among services required for the service provider based on preference data indicating preference of the service provider regarding a service and a price. The RNN learning model and the prediction learning model are based on a deep learning algorithm.