Personalized Reorder Layout Using Predictive Item Grouping
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Solution Overview
Problem
Online ordering systems often require users to manually browse through multiple pages to locate frequently reordered items, which is time-consuming and prone to forgetting items, leading to inefficiencies in the reordering process.
Innovation Solution
Implementing a single-select predictive platform model that uses machine learning to identify frequently reordered items and allows users to add them to their cart with a single click, utilizing recommendation servers and web servers to analyze user transaction data and predict item reordering probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually browse through multiple pages to locate frequently reordered items, then they can find all items they need, but it is time-consuming and prone to forgetting items
Solution Approach 1:
The system performs preliminary actions by automatically analyzing user transaction data, identifying frequently reordered items, and generating personalized reorder recommendations before the user needs to place an order. This eliminates the need for users to manually browse and search for items, as the system has already prepared the relevant items for reordering based on historical patterns.
Solution Approach 2:
The system enables self-service by automatically generating reorder recommendations without requiring active user input or manual searching. The system uses machine learning models to autonomously identify items the user is likely to reorder and presents them in a personalized interface, allowing users to quickly review and confirm their orders without manual effort.
2Productivity
If the system provides personalized reorder recommendations, then reordering efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of reorder recommendation into distinct functional modules: a recommendation server that analyzes transaction data, machine learning models that predict reorder likelihood, and a user interface that displays personalized recommendations. This segmentation allows each component to handle specific aspects of the complexity independently, making the overall system more manageable and maintainable.
Solution Approach 2:
The patent introduces intermediary components including recommendation servers and machine learning models that act as mediators between the user's ordering history and the personalized recommendations presented. These intermediaries process and interpret raw transaction data, transforming it into actionable insights without requiring the user interface to handle complex analysis directly.
Data Source
AI summary
A computer-implemented method including automatically generating predictions of a respective number of items that a user is likely to reorder in each of groups of the items that a user has ordered historically. The method also can include ranking the groups based on the predictions of the respective number of the items the user is likely to reorder in each of the groups. The method additionally can include transmitting for display to the user a user interface including the groups of the items. Other embodiments are described.