Dynamic Add-On Merchant Selection Model for Delivery Services
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Solution Overview
Problem
Current food delivery service applications lack efficient systems for dynamically managing and recommending add-on orders, failing to optimize user experience, cost, and delivery efficiency.
Innovation Solution
A computing system that analyzes user data and merchant-specific information to select and rank merchants for add-on orders, using machine-learned models to optimize the display of menu items based on conversion likelihood and fulfillment costs, thereby enhancing user experience and reducing computational and economic costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system displays all candidate merchants for add-on orders, then users have more options and better user experience, but data processing and bandwidth usage increase significantly
Solution Approach 1:
The system extracts and displays only the most relevant add-on merchants and menu items based on machine-learned predictions of user selection likelihood, rather than displaying all available options. This filtering approach reduces data transmission and processing while maintaining user experience quality.
Solution Approach 2:
The system dynamically changes the parameter of merchant selection by using machine-learned models to predict which merchants are most likely to be selected by users. This allows the system to adaptively adjust which merchants are displayed, optimizing both user experience and resource efficiency.
2Productivity
If the system uses machine-learned models to predict and filter merchants, then data processing efficiency improves and bandwidth usage decreases, but the complexity of the system increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine-learned models offline to predict merchant selection likelihood. During runtime, these pre-trained models quickly filter and rank merchants, reducing real-time computational complexity while maintaining high processing efficiency.
Solution Approach 2:
The machine-learned model acts as an intermediary between the user's primary order and the available add-on merchants. It processes and filters merchant data before presentation to the user, managing system complexity by handling the analytical burden in a dedicated layer.
3Adaptability or versatility
If the system personalizes the user interface based on user data analysis, then user experience is enhanced, but computational costs increase
Solution Approach 1:
The system applies local quality by personalizing only the relevant portions of the user interface - specifically the add-on merchant recommendations - rather than personalizing the entire application. This targeted approach enhances user experience while minimizing computational overhead.
Solution Approach 2:
The system performs partial personalization by focusing computational resources on predicting and displaying only the most likely add-on selections rather than analyzing all possible user preferences. This partial action approach provides meaningful personalization at reduced computational cost.
Data Source
AI summary
Systems and method for dynamically managing add-on orders within a delivery service application. For example, a computer-implemented method includes obtaining data indicative of a primary order request. The method includes selecting, ranking, and displaying menu items for add-on orders associated with a primary order. The method includes obtaining user data provided by a user through a user interface associated with a delivery service application. The method includes determining, in response to obtaining the user data, that the primary order request is eligible for an add-on order. The method includes determining merchants for the add-on order. The selected merchants can be determined from a plurality of candidate merchants based at least in part on analysis of merchant-specific data relative to the user data indicative of the primary order request. The method includes updating the user interface to display data associated with the one or more selected merchants for the add-on order.