Predictive User-Ad Matching List Generation
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Solution Overview
Problem
Current online advertisement technologies fail to optimally match users with relevant advertisements in real-time, leading to suboptimal exposure of ads to target audiences and user dissatisfaction.
Innovation Solution
A server-based method that predicts user access times using AI models, identifies target users based on profile and exposure history, and generates a user-advertisement matching list by prioritizing preferences, ensuring timely and relevant ad exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time advertisement selection is implemented, then advertisement exposure speed is improved, but user matching accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting user access times in advance and pre-generating user-ad matching lists before actual advertisement requests occur. This allows the system to prepare matched advertisement candidates ahead of time, so when a user actually accesses the online medium, the advertisement can be displayed immediately without real-time computation delays, thus maintaining both high speed and high accuracy.
2Device complexity
If traditional advertisement selection is used, then system complexity is reduced, but advertisement relevance to users deteriorates
Solution Approach 1:
The system segments the advertisement delivery process into distinct functional modules: a prediction module that forecasts user access times, a matching list generation module that creates user-ad matching lists, and an advertisement selection module that chooses final advertisements. This segmentation allows each module to specialize in one task, improving overall advertisement relevance while keeping the system structure manageable and not excessively complex.
3Measurement precision
If user profile analysis is performed, then advertisement targeting accuracy is improved, but processing time increases
Solution Approach 1:
The system performs user profile analysis and advertisement matching in advance, before actual advertisement requests are made. By predicting when users will access the online medium and pre-generating matching lists during off-peak times, the system avoids the time-consuming process of analyzing user profiles in real-time, thus maintaining high targeting accuracy while minimizing processing time delays.
Data Source
AI summary
A server and a method for generating a user-advertisement (user-ad) matching list for online advertisement is provided. The method includes obtaining profile information and advertisement exposure history information of users exposed to a plurality of advertisements through an ad impression server, obtaining advertisement information of an advertisement to be delivered by an advertiser, identifying candidate users for the advertisement of the advertiser among the users, based on the profile information of the users, predicting an access time when the candidate users will access the ad impression server, selecting target users to be provided with the advertisement of the advertiser among the candidate users, based on the predicted access time, obtaining a preference of each target user for an advertisement product of the advertiser, and generating the user-ad matching list by matching the advertisement of the advertiser with the target users, based on the preference.