Behavior Graph for Website User Relationship Analysis
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Solution Overview
Problem
Website designers face challenges in influencing user behavior to increase engagement and loyalty, as existing methods fail to effectively leverage user interactions to recommend relevant content and users, leading to suboptimal user experience and traffic retention.
Innovation Solution
A system and method that generates a behavior graph based on user interactions with content items, determining relationships among users and providing personalized recommendations and notifications to surface relevant content and users, thereby enhancing user engagement and website appeal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If website designers use traditional content recommendation methods, then implementation is simple, but user engagement and loyalty are insufficient
Solution Approach 1:
The patent replaces traditional mechanical recommendation systems with a behavior graph-based system that models user relationships and interactions. This substitution enables more intelligent, relationship-aware recommendations that improve engagement while the graph structure provides efficient query capabilities.
Solution Approach 2:
The behavior graph serves as an intermediary data structure between user interactions and recommendation generation. It captures relationships among users and content items, enabling the system to provide context-aware recommendations without requiring complex real-time analysis of all interaction data.
2Reliability
If website designers implement behavior-based relationship tracking, then user engagement improves, but data processing complexity increases
Solution Approach 1:
The system performs preliminary actions by continuously building and maintaining the behavior graph as users interact with content. This ongoing preprocessing of interaction data into structured relationships ensures that when recommendations are needed, the computational work has already been done, reducing real-time complexity.
Solution Approach 2:
The behavior graph structure enables self-service querying where the graph's inherent connectivity allows efficient traversal and pattern matching. The system leverages the graph's structure to automatically identify relevant users and content without requiring complex external processing logic.
3Ease of operation
If personalized recommendations are provided based on user relationships, then user experience improves, but computational requirements increase
Solution Approach 1:
The patent segments the recommendation problem into relationship-based queries on the behavior graph. By dividing the task into graph traversal operations and pattern matching, the system can leverage efficient graph algorithms that require less computational power than traditional machine learning approaches while still providing personalized recommendations.
Data Source
AI summary
A method for determining behavior-based relationships between website users is provided. The method may include monitoring activity of a plurality of users of a website to collect data regarding interaction with one or more content items associated with the website by the plurality of users. The method may further include analyzing the collected data to determine one or more relationships among the plurality of users based at least in part on the interaction with the one or more content items by the plurality of users. The method may additionally include generating a behavior graph having a structure defined based at least in part on the determined relationships. A corresponding system, apparatus, and computer program product are also provided.