Consideration Intent Classification for E-commerce Recommendations
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Solution Overview
Problem
E-commerce platforms face challenges in accurately classifying user shopping intent in real-time, impacting personalized recommendations and user experience, as existing systems lack effective methods to differentiate between low and high consideration events based on diverse user and item data.
Innovation Solution
A consideration intent system utilizing a computing device that receives event parameters, retrieves item and user intent values, and employs a machine learning algorithm to classify events as low or high consideration, generating recommended items by implementing specific recommendation models and updating scores in real-time based on user and item interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing classification systems are used to differentiate shopping events, then implementation is simple, but accuracy in classifying low and high consideration events is insufficient
Solution Approach 1:
The system transforms multiple parameters (user intent values, item intent values, device parameters, location data, cart information) into a unified consideration intent score through machine learning. This parameter transformation approach enables accurate differentiation between low and high consideration events by synthesizing diverse data sources into a comprehensive classification metric.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw event parameters and classification outcomes. This intermediary processes and synthesizes multiple input parameters (user behavior data, item characteristics, device information) to produce accurate consideration intent classifications, resolving the contradiction between simple implementation and high accuracy.
2Productivity
If real-time classification is implemented, then user experience is improved, but processing time and computational resources increase
Solution Approach 1:
The system pre-calculates and stores user intent values and item intent values before classification events occur. By maintaining pre-computed intent scores in databases, the system reduces real-time computational requirements, enabling fast classification decisions without sacrificing accuracy or requiring extensive processing time during actual shopping events.
3Measurement precision
If multiple data parameters are collected for accurate classification, then classification accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments data collection and processing into distinct modules: user intent value determination, item intent value determination, device parameter collection, and classification processing. This segmentation allows each component to handle specific data types independently, reducing overall system complexity while maintaining comprehensive data analysis for accurate classification.
Data Source
AI summary
A consideration intent system can include a computing device configured to receive an indication of an event occurring from a user device, obtain a set of parameters associated with the event and retrieve a set of item intent values corresponding to the set of items. The computing device is configured to determine a first value based on at least one parameter of the set of parameters and classify the event as one of: (i) low consideration intent and (ii) high consideration intent by inputting the set of item intent values and the first value as features to a machine learning algorithm. The computing device is configured to, based on the classification, identify a set of recommendation models, generate a set of recommended item identifiers by implementing at least one recommendation model of the set of recommendation models, and transmit the set of recommended item identifiers to the user device.