Adaptive Ad Selection Using User Engagement History
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Solution Overview
Problem
Mobile advertising faces challenges in timely and targeted delivery of advertisements to users, with existing systems struggling to provide relevant ads that engage users effectively.
Innovation Solution
A system utilizing an adaptive decision unit with processing logic to execute algorithms considering user engagement history, ad format, placement, and device characteristics to determine relevant ad content for optimal delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional mobile advertising systems distribute ads to users, then ads can be delivered to devices, but the ads are difficult to distribute to targeted users in a timely manner and users are not responsive or interested
Solution Approach 1:
The system dynamically changes multiple parameters including ad selection, ad format, ad placement, and timing based on user engagement history and real-time device state. The adaptive decision algorithm adjusts these parameters to optimize both distribution efficiency and engagement effectiveness simultaneously
Solution Approach 2:
The advertising system transitions from static ad distribution to dynamic adaptive decision-making. The system continuously learns from user interactions and adjusts ad delivery parameters in real-time, making the advertising process adaptive to individual user behaviors and device characteristics
2Reliability
If the system collects and processes multiple user data variables for personalized ad selection, then ad relevance and engagement improve, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex decision-making process into distinct modular components: user profile module, device profile module, adaptive decision algorithm, and ad delivery module. Each module handles specific data variables and functions, making the overall system more manageable and maintainable despite processing multiple user data dimensions
3Reliability
If real-time data processing is implemented for adaptive ad decisions, then ad relevance improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing user data to create comprehensive user profiles and device profiles before ad requests arrive. This pre-computation of user characteristics, engagement histories, and device specifications enables faster real-time decision-making when actual ad requests are processed
Solution Approach 2:
The system implements feedback loops where ad performance data and user interaction patterns are continuously fed back into the adaptive decision algorithm. This feedback mechanism refines the model over time, improving ad selection accuracy while the system learns to make more efficient decisions based on accumulated insights
Data Source
AI summary
Methods and systems are described for providing advertising services to devices with a customized adaptive user experience based on adaptive algorithms. In one embodiment, a system includes a storage medium to store one or more software programs and an adaptive decision unit coupled to the storage medium. The adaptive decision unit includes or is coupled to processing logic that is configured to execute instructions of at least one adaptive decision algorithm to obtain data for different variables including at least two of an advertisement (ad) engagement history for a user, application (app) engagement history for the user, and in-app purchase engagement history for the user when making an ad selection decision. The at least one adaptive decision algorithm determines an ad selection decision for at least one relevant ad or ad content served in an engaging manner to a device of the user.