Machine-Learning Sequential Content Recommendations Using Stage Graphs
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Solution Overview
Problem
Contemporary techniques for selecting content in online environments are inefficient and inaccurate, particularly for large-scale combinations of entities and interaction stages, often relying on manual methods that fail to provide customized and stage-specific content recommendations.
Innovation Solution
A content recommendation system utilizing machine-learning techniques, including a stage prediction module and a content sequencing module, to calculate transition probabilities for content items based on interaction ratios, identifying content that maximizes the likelihood of entities transitioning to desired interaction stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual content-selection techniques are used, then implementation simplicity is maintained, but content selection accuracy and customization deteriorate for large-scale combinations
Solution Approach 1:
The patent replaces manual content-selection techniques (mechanical/human process) with machine-learning-based automated selection (computational process). The system uses trained models to analyze entity characteristics and interaction stage data, automatically selecting optimized content sequences without human intervention, thereby achieving high accuracy for large-scale combinations while maintaining implementation feasibility through modular model architecture.
2Productivity
If manual content-selection techniques are used, then implementation simplicity is maintained, but productivity deteriorates due to inefficiency in analyzing large-scale combinations
Solution Approach 1:
The patent implements preliminary action by pre-training machine-learning models on extensive interaction data and entity characteristics before actual content selection. The models are prepared in advance with learned patterns and relationships, enabling rapid content selection during operation without time-consuming manual analysis, thereby achieving high productivity and reducing time loss for large-scale content selection tasks.
3Adaptability or versatility
If contemporary content selection techniques are used, then general applicability is maintained, but adaptability to specific interaction stages and entities deteriorates
Solution Approach 1:
The patent applies local quality by tailoring content selection to specific entities and interaction stages rather than using uniform approaches. The machine-learning models analyze entity-specific characteristics and stage-specific patterns, generating customized content sequences for each entity-stage combination. This localized adaptation achieves high versatility and customization while managing model complexity through modular architecture that reuses learned patterns across different contexts.
Data Source
AI summary
Certain embodiments involve using machine-learning methods to generate a recommendation for sequential content items. A method involves accessing a content item associated with an interaction stage in an online environment. A stage graph, which includes a ratio of interactions, of the content item is generated. An additional content item that includes additional stage-transition content is identified. A sequencing function outcome indicating a portion of the ratio of interactions is determined. A transition probability of receiving an interaction with stage-transition content and an additional interaction with the additional stage-transition content is calculated. A content provider system is caused to provide a recipient device with interactive content that includes the additional content item.