Dynamic Advertisement Rescheduling via User Interaction Logging
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Solution Overview
Problem
Current indoor Large Format Display (LFD) advertisement systems provide advertisements based on a predetermined schedule, ignoring user interest in Point Of Interest (POI) and failing to adapt to user preferences.
Innovation Solution
The system acquires user interaction data to reschedule advertisements dynamically, changing the display mode to an indoor space model to provide POI information and adjusting the advertisement schedule based on user-selected records, using a combination of user interaction logging, analysis of POI frequency, and real-time data from mobile devices to prioritize advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If advertisements are provided according to a predetermined advertisement schedule, then the advertisement providing system operates simply and stably, but the advertisements do not adapt to user preferences and lose relevance
Solution Approach 1:
The system logs user interactions with POI information in the indoor space model display mode and uses this feedback to dynamically adjust advertisement schedules. The advertisement providing server receives logging information about user-selected POIs and reschedules advertisements based on this feedback, creating a closed-loop system that adapts to user preferences while maintaining operational stability
Solution Approach 2:
The advertisement schedule transitions from a static predetermined schedule to a dynamic schedule that changes based on user interaction data. The system generates different advertisement schedules (first schedule for general users, second schedule for users showing specific POI interest) and switches between them based on real-time user behavior, making the system adaptable without requiring complete system redesign
2Loss of information
If the system provides only indoor geographic information using a simple image map, then the display mode remains simple, but the system fails to capture user interest and provide targeted advertisements
Solution Approach 1:
The system prepares an indoor space model display mode in advance that is specifically designed to capture user interaction data. Before providing advertisements, the system presents POI information in an engaging spatial model format that encourages user exploration and interaction, thereby preliminarily gathering the interest information needed for subsequent advertisement personalization
Solution Approach 2:
The indoor space model display mode acts as an intermediary between the simple image map and the advertisement providing system. It transforms basic geographic information into an interactive spatial model that naturally elicits user interactions, which are then captured and used to inform advertisement scheduling without requiring direct complex interaction mechanisms
3Productivity
If the system logs user interaction data and reschedules advertisements dynamically, then advertisement relevance to user preferences improves, but data processing requirements and system complexity increase
Solution Approach 1:
The system logs only specific user interactions related to POI selection in the indoor space model display mode, rather than capturing all possible user data. This partial logging approach focuses computational resources on the most relevant interaction types that directly inform advertisement scheduling decisions, reducing overall processing energy requirements while maintaining advertisement effectiveness
Solution Approach 2:
The advertisement providing system is segmented into distinct functional components: the indoor space model display mode for capturing interactions, the logging mechanism for data collection, and the rescheduling algorithm for advertisement optimization. This segmentation allows each component to operate independently with optimized resource usage, reducing the overall energy burden on the server while maintaining high advertisement effectiveness
Data Source
AI summary
Provided are an advertisement providing system and method. The advertisement providing method acquires information on a user interaction, acquires a second advertisement schedule which is generated by rescheduling a first advertisement schedule on the basis of the information on the user interaction, and provides an advertisement according to the second advertisement schedule.