Task Availability Prediction in Online Concierge Systems
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Solution Overview
Problem
Concierge systems often frustrate assistants due to unassigned tasks, leading to wasted preparation time and reduced morale, as they wait without success for task assignments.
Innovation Solution
An online concierge system with task availability assessment functionality that predicts task availability based on current context, using historical data and deep learning models to estimate the time until the next task assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If assistants wait for task assignments without prediction, then they can receive tasks when available, but they experience significant idle waiting time and reduced morale
Solution Approach 1:
The system performs preliminary assessment of task availability before assistants actually need to wait for tasks. By predicting whether tasks will be available in the near future and providing this information in advance, the system enables assistants to make informed decisions about whether to prepare for tasks or wait, thereby reducing unnecessary idle waiting time while maintaining reliable task assignment when needed
2Productivity
If assistants prepare for tasks without knowing availability, then they are ready when tasks arrive, but they waste preparation time when no tasks will be assigned
Solution Approach 1:
The system provides feedback to assistants about predicted task availability before they need to prepare for tasks. This feedback loop allows assistants to adjust their preparation behavior based on actual likelihood of task assignment, ensuring they prepare only when tasks are likely to be assigned, thereby maintaining high productivity while avoiding wasted preparation time
3Ease of operation
If the system provides detailed task availability predictions, then assistants can make informed decisions, but the system complexity increases
Solution Approach 1:
The system changes the parameters it monitors and predicts based on the specific needs and context of assistants. By adjusting which parameters are tracked (e.g., time-based predictions, location-based availability, task type preferences) and how predictions are formatted, the system provides detailed, actionable information to assistants while keeping the underlying complexity manageable through selective parameter focus
Data Source
AI summary
An online concierge system predicts how available tasks will be for a particular assistant in the assistant's current context. Task availability is computed differently in different embodiments. In a first embodiment, the task availability assessment functionality predicts an expected gap between demand for task performance and supply of assistants to perform those tasks. This expected gap is compared to historical gap values in a market segment (e.g., a particular geographical region during a particular span of time) to make a rough assessment of task availability relative to the average of that market segment. In a second embodiment, a set of features relevant to nearby retailer locations, the current geographic location, and/or the particular assistant is input to a deep learning model, which accordingly predicts a specific amount of time until the assistant receives a first task assignment.