Activity-Based Recommendation Network Segmentation
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Solution Overview
Problem
Existing product-based recommendation systems often fail to provide relevant suggestions to users as they rely on shopping habits of other users, which may not align with the targeted user's interests, leading to ineffective recommendations.
Innovation Solution
An activity-based recommendation system that creates a network of users based on their shared activities, allowing real-time recommendations to be generated based on current popular items within the user's activity-based network, with preferences filtered and weighted to ensure relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If product-based recommendation systems use shopping habits of other users to generate recommendations, then recommendations can be generated automatically, but the recommendations may not be relevant to the targeted user's interests
Solution Approach 1:
The patent segments users into different groups based on their activity patterns and characteristics. Instead of treating all users uniformly, the system creates distinct user segments or profiles that capture different shopping behaviors, interests, and preferences. This segmentation allows the recommendation system to match users with more appropriate reference groups, improving recommendation relevance while maintaining automation.
Solution Approach 2:
The patent applies local quality by providing different recommendation strategies to different user segments. Each user group receives tailored recommendations based on their specific characteristics and activity patterns. The system adjusts the recommendation approach locally for each user segment rather than applying a single uniform method to all users, thereby improving relevance for each specific group.
2Reliability
If activity-based networks are created and maintained in real-time, then recommendation relevance improves, but system complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing user activity profiles, segmentation criteria, and recommendation rules before they are needed for generating actual recommendations. User segments are pre-defined based on activity patterns, and reference groups are pre-identified. This preliminary processing reduces the computational complexity during real-time recommendation generation, as the system only needs to match users with pre-established segments rather than performing complex analysis on-the-fly.
Solution Approach 2:
The patent introduces intermediaries in the form of user profiles, activity segments, and reference group mappings that mediate between raw user activities and final recommendations. These intermediary structures simplify the system architecture by providing layered abstractions - instead of directly comparing all user activities, the system uses intermediate segment representations that capture essential patterns while filtering out noise and complexity.
Data Source
AI summary
Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.