Household Device-User Graph for Targeted Content Delivery
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Solution Overview
Problem
Existing content delivery systems to target households are ineffective as they lack knowledge about the electronic devices used by household users and their associations, leading to wasted resources as content is often ignored by users.
Innovation Solution
An online system generates a household device-user graph, linking devices with user profiles to identify and deliver targeted content based on usage patterns and user interests, ensuring content is delivered to the appropriate devices and users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If content items are sent to target household without knowing device-user associations, then content delivery coverage is maximized, but resource efficiency deteriorates due to wasted resources on ignored content
Solution Approach 1:
The system performs preliminary actions by building device-user graphs in advance that map household devices to user profiles before content delivery occurs. This pre-established knowledge base enables efficient content targeting without requiring complex real-time analysis during delivery, thus improving resource efficiency while maintaining manageable system complexity
Solution Approach 2:
The device-user graph serves as an intermediary data structure that mediates between content providers and household devices. It contains pre-computed associations between devices and users, allowing content to be efficiently routed to the right devices without requiring direct complex interactions between content delivery systems and household device ecosystems
2Productivity
If content delivery targets all households without device-user knowledge, then delivery reach is improved, but content relevance deteriorates leading to user disengagement
Solution Approach 1:
The system incorporates feedback mechanisms where user interactions with content and device usage patterns are continuously collected and used to refine device-user graph associations. This feedback loop enables the system to learn and adapt to user preferences over time, improving content relevance and delivery efficiency while capturing valuable user preference information
Solution Approach 2:
The system changes parameters by dynamically updating device-user graph associations based on observed usage patterns and user behavior. Rather than using static device-household mappings, the system adjusts the strength and nature of device-user associations over time, enabling more accurate content targeting that adapts to changing user preferences and behaviors
Data Source
AI summary
An online system generates a household device-user graph, which links one or more household devices in a household with one or more users, each of whom having a user profile in the online system. The household device-user graph can be used for effective content delivery to users of the online system. The device-user graph generated by the online system describes connections between household device users and household devices in the target household and usage of the household devices by the household device users. Each household device user represented in the device-user graph is connected to one or more household devices represented in the device-user graph. The online system determines whether one or more household device users identified in the device-user graph are users of the online system, and updates the user profiles of the identified household device users in response to a determination that the identified household device users are users of the online system.