Personalized Advertisement Timing via User Profile Segmentation
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Solution Overview
Problem
Existing advertisement timing in media content is determined by content providers based on projected viewer interest, which may not align with individual user preferences, leading to suboptimal ad placement.
Innovation Solution
A media guidance application parses media assets into segments, analyzes user profiles and engagement levels, and determines personalized advertisement timing based on user preferences and engagement, potentially overriding or supplementing content provider-determined ad placements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If advertisement timing is determined by content provider based on projected viewer interest, then ad placement can be made based on general content popularity, but individual user preferences are not considered leading to suboptimal ad placement
Solution Approach 1:
The media asset is parsed into multiple time segments, and user profile is segmented into various attributes (demographics, preferences, viewing habits). This segmentation enables targeted matching of advertisements to specific time segments based on individual user characteristics rather than generic content-based timing.
Solution Approach 2:
User profiles are created and maintained in advance with comprehensive information about user preferences, demographics, and viewing habits. This preliminary preparation of user data enables rapid and accurate advertisement timing decisions without requiring complex real-time analysis during content playback.
2Reliability
If advertisement is displayed during time segments that match user profile preferences, then ad relevance to user interests is improved, but ad placement may conflict with content provider's intended ad slots
Solution Approach 1:
The advertisement timing system dynamically adjusts ad placement based on real-time analysis of user engagement with content and current user profile attributes. Rather than fixed ad slots, the system continuously determines optimal timing by comparing user profile against metadata of different time segments, allowing flexible adaptation to both user interests and content flow.
Solution Approach 2:
The system incorporates user engagement feedback (such as watching behavior, interactions with content) to refine advertisement timing decisions. This feedback mechanism ensures that ads are placed during segments where users are most engaged and likely to be receptive, improving relevance while maintaining efficient ad slot utilization through data-driven optimization.
3Adaptability or versatility
If multiple users with different profiles watch the same media asset, then personalized ad timing can be provided for each user, but determining suitable time points for different users becomes more complex
Solution Approach 1:
When multiple users are detected, the system segments the audience and applies individualized advertisement timing for each user based on their respective profiles. The media asset is divided into time segments, and each user receives ads during segments that match their personal preferences, enabling simultaneous personalized advertising experiences without requiring a single generic timing solution.
Solution Approach 2:
The advertisement timing system is designed to handle multiple user profiles through a universal framework that processes each user's profile independently against the same content metadata. This multi-functional capability allows the system to scale from single-user to multi-user scenarios without fundamental redesign, managing complexity through consistent application of the profile-matching algorithm across multiple users.
Data Source
AI summary
Systems and methods are provided herein for determining personalized timing for generating for display advertisements to users. Rather than an expert determining time segments of a media asset most suitable for presenting advertisements to users, the most suitable time segments in a media asset for presenting advertisements to users may be customized based on a user's profile information and/or the user's level of engagement in a media asset. The media guidance application may parse a media asset into multiple time segments and determine one or more time segments associated with metadata that matches content characteristics preferred by the user. One or more advertisements may be presented to the user in these time segments determined by the media guidance application instead of the time segments determined by the expert.


