Cluster-Based User Interest Assessment for Recommendation Accuracy
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Solution Overview
Problem
Existing recommendation systems face challenges in accurately identifying user preferences without explicit input, leading to poorly tailored recommendations due to inclusion of gift purchases, shared accounts, and changing user interests.
Innovation Solution
Implementing item clustering techniques to assess user interests by subdividing purchase histories into clusters, excluding outlier items, and using cluster-level metadata to improve recommendation accuracy and user management interfaces for personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If item clustering techniques are applied to automatically filter irrelevant items, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the purchase history into distinct clusters based on item characteristics and user behavior patterns. By dividing the data into homogeneous groups (e.g., frequent purchases vs. occasional purchases, gift items vs. personal items), the system can automatically identify and filter irrelevant items without requiring complex manual intervention, thus improving recommendation accuracy while managing computational complexity through structured data organization
Solution Approach 2:
The clustering algorithm operates autonomously to identify and filter irrelevant items based on predefined criteria such as purchase frequency, item category, and temporal patterns. The system self-adjusts by analyzing cluster characteristics and automatically determining which items to exclude from recommendations, reducing the need for manual user input while maintaining high recommendation accuracy
2Reliability
If users manually review and edit their item collections, then recommendation quality improves, but user time consumption increases
Solution Approach 1:
The system performs self-service by automatically clustering and filtering items based on aggregated user behavior data and predefined criteria. Instead of requiring each user to manually review their entire purchase history, the algorithm autonomously identifies patterns (such as frequent purchases, gift items, or seasonal items) and filters them automatically, significantly reducing user time consumption while maintaining high recommendation quality
Solution Approach 2:
The system performs preliminary clustering and filtering actions before generating recommendations. By pre-organizing the purchase history into meaningful clusters and pre-identifying potentially irrelevant items based on cluster characteristics, the system prepares cleaned data in advance, eliminating the need for users to manually review and edit individual items while ensuring high recommendation accuracy
3Measurement precision
If explicit user input is required to identify relevant items, then recommendation precision improves, but ease of operation deteriorates
Solution Approach 1:
The clustering system operates without requiring explicit user input to identify relevant items. Instead, it automatically analyzes purchase history patterns, item characteristics, and user behavior data to determine which items are relevant and should be included in recommendations. This self-service approach maintains high recommendation precision by using sophisticated algorithms that infer user preferences from implicit behavior data, while dramatically improving ease of operation by eliminating manual item review requirements
Data Source
AI summary
Computer-implemented processes are disclosed for clustering items and improving the utility of item recommendations. One process involves applying a clustering algorithm to a user's collection of items. Information about the resulting clusters is then used to select items to use as recommendation sources. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to attach cluster-level metadata, such as by rating or tagging entire clusters of items. The resulting metadata may be used to improve the recommendations generated by a recommendation engine. Another process involves forming clusters of items in which a user has indicated a lack of interest, and using these clusters to filter the output of a recommendation engine. Yet another process involves applying a clustering algorithm to the output of a recommendation engine to arrange the recommended items into cluster-based categories for presentation to the user.