Messaging Platform Ad Selection via Temporal Feature Segmentation
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Solution Overview
Problem
Current automatic techniques for classifying and selecting content in Internet services, such as textual, audio, and video content, face challenges in efficiently determining relevant advertisements to associate with user-generated content, particularly in real-time messaging platforms, where processing burdens and ad fatigue are significant.
Innovation Solution
A real-time messaging platform employs a modular system that includes targeting, filtering, prediction, and ranking processes to select candidate messages for inclusion in user message streams based on bid prices, engagement likelihood, and user preferences, utilizing features extraction and analytics to optimize ad placement and reduce processing loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional automatic classification techniques are used for content selection in messaging platforms, then content can be categorized and advertisements can be associated, but processing burden increases and ad fatigue occurs
Solution Approach 1:
The patent segments the content classification process into multiple independent modules: feature extraction module that identifies temporal features from message data, classification module that processes extracted features, and selection module that chooses content for display. This segmentation distributes processing burden across specialized components rather than requiring a single complex system, thereby maintaining productivity while reducing overall device complexity.
Solution Approach 2:
The system performs preliminary feature extraction and temporal analysis on message content before the actual classification and advertisement selection occurs. By pre-processing and extracting relevant temporal features (such as message timing patterns, user activity cycles) in advance, the classification module receives already-processed data, significantly reducing its processing burden while maintaining high classification efficiency.
2Quantity of substance
If more advertisements are displayed to increase revenue, then advertiser ROI improves, but user engagement decreases due to ad fatigue
Solution Approach 1:
The patent implements dynamic advertisement selection that adapts to user behavior patterns and temporal context. The system continuously monitors user engagement metrics and adjusts advertisement display strategies in real-time, optimizing the balance between advertisement volume and user engagement. This dynamic approach allows the system to maintain higher advertiser ROI by displaying relevant ads while preventing ad fatigue through context-aware suppression of excessive or irrelevant advertisements.
Solution Approach 2:
The system incorporates feedback loops that monitor user responses to displayed advertisements and use this information to adjust future advertisement selection. By analyzing engagement patterns, click-through rates, and user behavior changes, the system learns which advertisement strategies work best and automatically adjusts the quantity and timing of ad displays to maximize ROI while maintaining user engagement and preventing ad fatigue.
3Measurement precision
If real-time processing is implemented for ad selection, then ad relevance to user context improves, but processing speed and system resource consumption increase
Solution Approach 1:
The system performs preliminary extraction of temporal features and user context information in advance of the actual advertisement selection moment. By pre-processing message data, user profiles, and behavioral patterns to extract relevant temporal features (such as message timing, user activity cycles, contextual patterns) beforehand, the system reduces the computational burden during real-time ad selection, thereby maintaining high ad relevance accuracy while improving processing speed and reducing system resource consumption.
Solution Approach 2:
The patent applies different processing levels to different aspects of ad selection: full temporal feature extraction and analysis is applied only to messages and contexts where it will significantly impact ad relevance, while simpler heuristics are used for routine cases. This localized application of complex processing ensures high precision for critical decisions while maintaining fast processing speeds for standard situations, optimizing the balance between ad relevance accuracy and processing speed.
Data Source
AI summary
A real-time messaging platform allows advertiser accounts to pay to insert candidate messages into the message streams requested by account holders. To accommodate multiple advertisers, the messaging platform controls an auction process that determines which candidate messages are selected for inclusion in a requested account holder's message stream. Selection is based on a bid for the candidate message, the message stream that is requested, and a variety of other factors that vary depending upon the implementation. The process for selection of candidate messages generally includes the following steps, though any given step may be omitted or combined into another step in a different implementation: targeting, filtering, prediction, ranking, and selection.