Multi-Slot Content Ranking with Interaction Effects
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Solution Overview
Problem
Current social networking services face challenges in ranking content items for display in user feeds, as they often prioritize single objectives without considering user interaction effects across multiple content slots, leading to suboptimal engagement and revenue generation.
Innovation Solution
The Content Optimization Engine employs a machine learning data model and multi-objective optimization algorithm to rank content items, balancing engagement and revenue goals by determining display probabilities and interaction effects across multiple content slots, ensuring a diverse mix of content types, including sponsored items, to maximize user interaction and revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content items are ranked based on single objective optimization, then the optimization process is simple, but user engagement and revenue generation are suboptimal
Solution Approach 1:
The patent segments the content feed into multiple discrete slots and applies separate optimization to each slot while considering interactions. The optimization is divided into slot-specific decision variables and constraints, allowing the system to handle complexity through structured decomposition rather than monolithic optimization.
Solution Approach 2:
The patent transitions from single-objective to multi-objective optimization by adding revenue as a second objective dimension alongside engagement. This dimensional expansion allows simultaneous optimization of both metrics through a unified framework that balances user experience with monetization goals.
2Productivity
If content items are ranked without considering interaction effects, then the ranking process is computationally efficient, but user engagement is suboptimal
Solution Approach 1:
The patent performs preliminary actions by pre-defining the slot structure, content item features, and interaction effect models before optimization. Historical interaction data is pre-processed to inform the optimization algorithm, enabling it to account for interaction effects without excessive computational burden during real-time ranking.
Solution Approach 2:
The patent incorporates feedback mechanisms by using historical user interaction data to inform the optimization algorithm. The system learns from past user behaviors and interaction patterns, feeding this information back into the optimization process to improve engagement while accounting for how content items interact with each other in the feed.
3Adaptability or versatility
If content slots are optimized independently, then the optimization is computationally simpler, but diversity of content types is reduced
Solution Approach 1:
The patent creates a universal optimization framework that simultaneously handles multiple content types (sponsored, organic, mixed) across multiple slots. The same optimization algorithm and objective function apply universally to all slots, ensuring consistent diversity goals are met throughout the feed while maintaining computational tractability through standardized processing.
Data Source
AI summary
A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Content Optimization Engine that determines a display probability for each content item in a set of content items. Each respective display probability corresponds to a given content item's probability of display in a specific content slot of a plurality of content slots in a social network feed of a target member account in a social network service. The Content Optimization Engine calculates a selection probability for each content item in an ordered set of the content items, based on each display probability and a set of interaction effects. The Content Optimization Engine causes display of the ordered set of content items in the target member account's social network feed based on satisfaction of the first and second targets.