Invitational Content Delivery via User Intent Prediction
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Solution Overview
Problem
Content providers face challenges in delivering invitational content effectively, as conventional methods lack the ability to predict user intent accurately, leading to inefficient delivery and wasted resources, as they often send content to users who are not interested or not in the right stage of the purchasing process.
Innovation Solution
A system and method that analyze a user's journal of events to identify partial action sequences and compute proximity scores, allowing for the selection and delivery of invitational content based on predicted user intent, ensuring that content is delivered when the user is most likely to engage with it.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If invitational content is delivered using conventional methods without user intent prediction, then content delivery can be performed with simple systems, but the relevance and conversion rate of delivered content deteriorates
Solution Approach 1:
The system performs preliminary analysis of user event journals to identify partial action sequences and compute proximity scores before delivering invitational content. This advance preparation enables the system to predict user intent and select appropriate content, improving delivery effectiveness without requiring complex real-time processing during content presentation
Solution Approach 2:
The user's interaction history is segmented into discrete events stored in a journal, which are then analyzed to identify partial action sequences. This segmentation allows the system to process user behavior in manageable units and compute proximity scores for different user modes, enabling sophisticated intent prediction through structured data analysis
2Loss of energy
If invitational content is delivered without analyzing user intent, then resource utilization can be maintained at simple levels, but wasted resources on irrelevant content delivery increases
Solution Approach 1:
The system maintains an event journal that records user interactions and uses this feedback to compute proximity scores and predict user intent. This feedback mechanism allows the system to learn from past user behavior and continuously improve content selection, reducing resource waste on irrelevant deliveries while increasing overall delivery efficiency
Solution Approach 2:
The system changes the parameter of content delivery from static, rule-based selection to dynamic selection based on computed proximity scores. By adjusting content delivery decisions based on these scores derived from user event analysis, the system optimizes resource utilization by delivering content only when user intent indicates high probability of engagement
3Measurement precision
If the system delivers content based on predicted user intent using event journal analysis, then content relevance improves, but the complexity of content selection and delivery increases
Solution Approach 1:
The system performs preliminary computation of proximity scores for different user modes by analyzing the event journal before content delivery decisions are made. This advance computation of intent prediction metrics simplifies the actual content selection process, as the system can then directly match high-scoring user modes with appropriate invitational content without requiring complex real-time decision algorithms
Solution Approach 2:
The event journal serves as an intermediary data structure that captures user interactions and enables intent prediction without requiring direct complex analysis during content delivery. The journal acts as a buffer that pre-processes user behavior data, allowing the system to make accurate content selection decisions based on stored proximity scores rather than performing complex analysis at the moment of content delivery
Data Source
AI summary
Systems and methods are provided for delivering invitational content based on a prediction of the intent of a user. In particular, partial action sequences are identified in a journal of events associated with the user. Thereafter, the partial sequences are analyzed and scored based on their degree of proximity to completion. Based on this scoring, a queue of invitational content can then be generated, where the scoring can be used to select and order the invitational content for the user. In some configurations, the information in the journal can be used to determine future time intervals at which sequences will be completed by the user. Consequently, a queue can be adjusted to provide delivery of appropriate invitational content at these future time intervals.