Clustering User Interactions for Shopping Missions
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Solution Overview
Problem
Users face difficulties in finding desired items on web pages, especially when their intent is unclear, as existing systems lack effective methods to cluster interactions and identify shopping missions based on item attributes and user behavior.
Innovation Solution
A system that associates user interactions with clusters of shopping missions by analyzing interaction histories, item attributes, and clustering algorithms to identify related shopping missions and provide personalized recommendations, advertisements, and buying guides.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the system provides multiple web pages with various items, then the quantity of items available to users increases, but it becomes more difficult for users to find their desired items when their intent is unclear
Solution Approach 1:
The patent segments user interactions into distinct clusters based on item attributes and interaction patterns. By dividing the large set of items and interactions into smaller, organized clusters, the system makes it easier to navigate and find desired items even when user intent is unclear. Each cluster represents a coherent group of related items and interactions, reducing the cognitive load on users.
Solution Approach 2:
The patent introduces clustering algorithms and interaction analysis as intermediary processes between users and items. These intermediaries automatically analyze user behavior patterns, item attributes, and interaction histories to organize and present relevant items, eliminating the need for users to manually search through all available items.
2Measurement precision
If the system tracks detailed interaction histories to understand user intent, then the precision of identifying user needs improves, but the complexity of the system increases
Solution Approach 1:
The system employs self-service mechanisms where clustering algorithms automatically organize interactions and identify patterns without requiring manual intervention. The system serves itself by autonomously analyzing interaction data, extracting features, and generating clusters that reflect user intent, thereby reducing operational complexity while maintaining high precision.
Solution Approach 2:
The patent transforms raw interaction data into meaningful clusters by changing the parameters of analysis. Instead of treating all interactions uniformly, the system adjusts analysis parameters based on interaction types, item attributes, and user behavior patterns, enabling precise intent identification through adaptive parameter selection rather than increasing overall system complexity.
3Ease of operation
If the system provides personalized recommendations and content based on clustering, then the user experience improves, but the computational resources required increase
Solution Approach 1:
The system performs preliminary clustering and organization of interactions and items before users need recommendations. By pre-processing and organizing data into coherent clusters based on item attributes and interaction patterns, the system reduces the computational burden during actual recommendation generation, delivering personalized content efficiently while maintaining high user experience quality.
Data Source
AI summary
Techniques for identifying clusters of user interactions and shopping missions may be provided. For example, the system may receive a history of interactions between a user and one or more network pages. The system may identify a most recent event from the history of interactions and identify a cluster that includes other events from the history of interactions that are of a same category as the most recent event. The determination of the cluster may be based in part on item attributes associated with the item presented on the at least one of the one or more network pages. The most recent event may then be associated with the cluster. In some examples, a shopping mission is determined and one or more notifications are provided to a user, merchant, or electronic marketplace in association with the identified shopping mission.