Voice Content Attribution via Session Identifiers
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Solution Overview
Problem
Current natural language processing systems lack an effective mechanism for attributing user selections of content recommended by third-party skills to the originating skill developer, especially when the content is consumed on a different platform, leading to difficulties in tracking recommendations and providing appropriate attribution.
Innovation Solution
A content attribution platform is introduced that allows skill developers to register their skills and specify content items for attribution, generating an affiliate ID for each skill. This ID is used to associate voice-based content recommendations with the skill developer, enabling accurate attribution when users select recommended content, using a unique session identifier to validate and credit the developer's account.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a content attribution platform is introduced to track recommendations, then attribution accuracy is improved, but system complexity increases
Solution Approach 1:
A content attribution platform is introduced as an intermediary system between the speech processing system and content delivery platforms. This mediator receives content recommendations from skills, generates session identifiers, and tracks user selections to attribute them back to the appropriate skill developers, thereby enabling accurate attribution without requiring direct integration between all components.
Solution Approach 2:
The attribution system is segmented into distinct functional components: skill registration module, session identifier generation module, content recommendation module, user selection tracking module, and attribution crediting module. This segmentation allows each component to perform its specific function independently, managing overall system complexity while maintaining attribution accuracy.
2Reliability
If session identifiers are used to track content selections, then attribution reliability is improved, but information processing overhead increases
Solution Approach 1:
Session identifiers are generated and associated with skill developer identifiers before content is recommended to the user. This preliminary action ensures that when a user selects content, the attribution can be reliably determined without requiring complex real-time analysis, thereby reducing information processing overhead during the selection event.
Solution Approach 2:
The session identifier acts as a simplified copy or representation of the complex attribution relationship between skill, user, and content. Instead of tracking all detailed interactions, the system uses this identifier copy to reliably attribute selections, reducing the information processing burden while maintaining attribution reliability.
Data Source
AI summary
Devices and techniques are generally described for voice-based content attribution for speech processing applications. In some examples, a request for voice-based content may be received from a first speech processing skill. First identifier data associated with the first speech processing skill may be received. A determination may be made that first content that is associated with the request. Voice-based output data describing the first content may be generated. A selection of the first content may be received. Attribution data may be determined based at least in part on the selection of the first content and the first identifier data. The attribution data may be sent to a remote computing device.