Sequential Event Pattern Analysis for Context-Aware Content Delivery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing content delivery systems primarily rely on isolated user activity data, failing to provide timely and relevant content that accounts for the user's longitudinal behavior patterns over time, which limits their effectiveness in offering context-specific recommendations.
Innovation Solution
A method that detects event sequences involving current and prior events, correlates these with frequent sequential patterns, and predicts the next event to provide relevant content, using a processor-based system that incorporates crowd-sourced data and context-specific behavior analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If content delivery systems use isolated user activity data, then the system complexity is low, but the relevance and timeliness of content recommendations deteriorate
Solution Approach 1:
The patent transitions from analyzing isolated user activities (single dimension) to analyzing sequential event patterns across multiple time points (temporal dimension). By incorporating the temporal sequence of events and using Markov models to capture state transitions over time, the system recovers longitudinal behavior patterns that were lost in traditional isolated activity analysis, thereby improving recommendation relevance without proportionally increasing complexity.
2Loss of information
If content delivery systems analyze longitudinal behavior patterns, then the relevance of content recommendations improves, but the computational complexity and data processing requirements worsen
Solution Approach 1:
The patent transforms raw event sequence data into standardized state representations with defined transition probabilities. By parameterizing user behavior into discrete states and transition probabilities within a Markov framework, the system captures complex longitudinal patterns using manageable mathematical parameters, reducing the computational burden of analyzing raw sequential data while preserving behavioral accuracy.
Solution Approach 2:
The patent creates simplified models (Markov chains) that replicate essential behavioral patterns without requiring full analysis of original complex event sequences. These model copies capture the probabilistic structure of user behavior, enabling efficient prediction and recommendation generation while avoiding the computational expense of processing complete historical event data for each recommendation query.
3Speed
If the system provides content based on isolated user activity, then the response time is fast, but the timeliness and usefulness of content delivery deteriorates
Solution Approach 1:
The patent pre-computes transition probabilities and identifies frequent sequential patterns from historical event data before they are needed for recommendations. By performing this analysis in advance and storing the resulting behavioral models, the system avoids computationally intensive real-time analysis when generating recommendations, thus maintaining fast response times while incorporating rich longitudinal behavior information for timely and relevant content delivery.
Data Source
AI summary
Providing sequential behavior-based content may include detecting, using a processor, an event sequence involving at least one device of a user, wherein the event sequence includes a current event and a prior event, correlating, using the processor, the event sequence with a frequent sequential pattern selected from a plurality of frequent sequential patterns associated with the user, and predicting, using the processor, a next event for the user according to a plurality of ordered events specified by the selected frequent sequential pattern. Responsive to predicting the next event, content relating to the next event may be provided to the device using the processor.