LLM-Generated Audience Segments for Cold-Start Targeting
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Solution Overview
Problem
Existing machine learning solutions struggle to recommend appropriate audience segments for content distribution, especially in 'cold-start' scenarios where no prior entity information is available, leading to inefficiencies and inaccurate results.
Innovation Solution
A framework utilizing a large language model (LLM) to generate targeting criteria for audience segments based on media content, enhanced by taxonomy information, enabling end-to-end automation across multiple components in an online network through a flywheel of machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning models are used for audience recommendation, then the system requires prior entity information and historical data, but this causes failure in cold-start scenarios where no prior information is available
Solution Approach 1:
The system performs preliminary actions by pre-training the language model on extensive media content and taxonomy information before deployment. This pre-training enables the model to generate meaningful audience segments even without prior entity-specific data, effectively preparing the system to handle cold-start scenarios where no historical information exists.
Solution Approach 2:
A language model serves as an intermediary between the media content and the audience segmentation process. This intermediary translates unstructured media content into structured audience segment predictions, bridging the gap between content features and target audience identification without requiring direct historical entity data.
2Measurement precision
If manual audience selection processes are used, then the system can handle complex targeting criteria, but this increases operational complexity and time consumption
Solution Approach 1:
The system enables self-service by automatically generating audience segments from media content without requiring manual configuration. The language model autonomously processes content features and produces targeted audience segments, eliminating the need for manual audience selection while maintaining high precision in targeting accuracy.
Solution Approach 2:
The patent replaces manual mechanical processes of audience selection with an automated language model-based system. Instead of manual analysis and configuration of targeting criteria, the system uses natural language processing and machine learning to automatically generate audience segments, significantly reducing time consumption while preserving targeting precision.
3Productivity
If multiple disparate components are integrated for end-to-end automation, then the system achieves unified goals and improved efficiency, but this increases system complexity
Solution Approach 1:
The system merges multiple previously disparate components into a unified end-to-end automated framework. By combining media content processing, language model inference, taxonomy integration, and audience segment generation into a single integrated system, the patent achieves improved productivity while managing complexity through cohesive architecture design.
Solution Approach 2:
The language model serves multiple functions within the system: it processes media content, understands taxonomy relationships, generates audience segments, and adapts to different content types. This multi-functionality reduces the need for separate specialized components, achieving end-to-end automation with controlled complexity through a universal processing engine.
Data Source
AI summary
In an example embodiment, a framework is presented that utilizes a large language model (LLM) to aid in the generation of targeting criteria, namely in generating portions of information that will be used to generate a target audience for a particular media item, based in part on the media item itself. More specifically, the LLM is used to produce a suggestion of one or more facets of a target audience, wherein each facet is a category of information for users within the target audience. The LLM is then also used to produce a plurality of segments within the one or more suggested facets. The produced facets and segments may then be used to create a target audience for the media item, even in a cold-start environment where no information about a desired audience is provided by the entity wishing to distribute the media item.