Context-Aware Attribute Selection for Online Concierge Interfaces
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Online concierge systems face challenges in effectively displaying item attributes due to limited interface space, leading to excessive information and impaired user navigation, especially when attributes provide different levels of information across various contexts, making it difficult to maximize information presentation.
Innovation Solution
The system employs an attribute selection model that evaluates information gain to determine the most relevant attributes to display for each item based on context, using training examples to optimize attribute selection and improve user interaction by prioritizing information density and clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple attributes of an item are displayed in the interface, then information about the item is increased, but the interface becomes more cumbersome and difficult to navigate
Solution Approach 1:
The system dynamically changes the parameter of attribute selection based on context. A machine-learned model evaluates information gain for different attributes in different contexts (search results, recipe, category browsing) and selects the optimal attribute to display, transforming the static display approach into a dynamic one that adapts to user needs and interface context.
2Ease of operation
If a specific attribute is displayed for an item across interfaces in different contexts, then navigation is simplified, but the information provided to the user varies and may not be optimal for each context
Solution Approach 1:
The system applies local quality by tailoring the attribute display to each specific context. Instead of using a uniform attribute across all interfaces, the machine-learned model determines the most informative attribute for each context type (search results, recipe, category browsing), ensuring that the displayed attribute is locally optimized for that particular interface context.
3Adaptability or versatility
If heuristically selecting an attribute of an item is used for context-specific display, then some context adaptation is achieved, but the system is difficult to scale for increasing numbers of items or attributes
Solution Approach 1:
The system replaces manual heuristic selection with an automated machine-learned model. This substitution transforms the attribute selection process from a complex,难以 scaling manual approach to an automated computational system that can efficiently handle increasing numbers of items and attributes without proportional increases in system complexity.
Data Source
AI summary
An online concierge system presents items to a user in one or more interfaces and maintains various attributes for each item. To optimize information about items in an interface, when the online concierge system receives a request for an interface, the online concierge system determines a context for the interface and a set of items to display in the interface from the request. For an item displayed by the interface, the online concierge system applies a trained attribute selection to each combination of the item, an attribute of the item, and the context for the interface to determine an information gain to the user from displaying the attribute of the item along with the item in the interface with the context. Based on the information gains, the online concierge system selects an attribute to display in the interface in conjunction with the item.