Dynamic Advertisement Allocation Using User Feedback
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Solution Overview
Problem
Existing TV advertisement placement methods do not effectively utilize user feedback to target advertisements based on user interests, leading to inefficient allocation of advertisement time slots and increased costs for advertisers and TV stations, as they rely solely on TV program ratings rather than user engagement and preferences.
Innovation Solution
A system and method that utilize an image object marking and response element to track user interest in TV program objects, collect feedback, and an advertisement auto-matching element to compute correlation scores between TV program objects and advertisements, dynamically determining the most relevant advertisements to broadcast based on user feedback, thereby optimizing advertisement placement and time slots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If advertisement placement is based on TV program rating, then advertisement fee increases with program rating, but advertisement effectiveness decreases because it does not consider user interest
Solution Approach 1:
The system collects user feedback information about TV programs and uses this feedback to dynamically adjust advertisement placement. The feedback mechanism allows the system to learn user preferences and interests, then match advertisements accordingly, resolving the contradiction between high program rating and low advertisement effectiveness by introducing user response feedback loops.
Solution Approach 2:
The advertisement placement system transitions from static (pre-purchased time slots based on fixed program ratings) to dynamic (real-time adjustment based on user feedback). The system dynamically determines advertisement content and placement time slots according to actual user interest, allowing the advertisement strategy to adapt to changing user preferences while maintaining effectiveness.
2Ease of manufacture
If TV station allocates advertisement time slots based on program rating, then allocation process is simple, but user engagement with advertisements decreases
Solution Approach 1:
The system enables users to indirectly select advertisements through their viewing behavior and feedback. Instead of manual allocation by TV stations, the system automatically matches advertisements to users based on their demonstrated interests, allowing the advertisement selection process to serve itself through automated feedback-driven matching.
Solution Approach 2:
The manual mechanical process of TV station allocation based on program ratings is replaced with an automated information processing system that uses user feedback data. The system substitutes the simple allocation mechanism with a sophisticated matching algorithm that processes user responses to determine optimal advertisement placement, thereby increasing user engagement while maintaining ease of operation.
3Reliability
If advertiser pre-purchases time slot, then advertisement placement is secured, but flexibility to adapt to user interest is lost
Solution Approach 1:
The system performs preliminary collection and analysis of user feedback information before finalizing advertisement placement. By pre-processing user responses and interest data, the system prepares adaptive advertisement recommendations in advance while maintaining the security of allocated time slots. This preliminary action allows the system to secure placement while preserving adaptability to user interests.
Solution Approach 2:
The system transforms the static pre-purchased time slot arrangement into a dynamic placement strategy. While time slots are secured in advance, the actual advertisement content and timing are dynamically adjusted based on real-time user feedback, allowing the system to maintain placement security while adapting to changing user interests and preferences.
Data Source
AI summary
According to one exemplary embodiment of an advertisement allocating system, an image object marking and response element receives a TV program signal and then marks at least one object appearing in the TV program during a current time slot; a user element tallies all the users interested in the object, and collects all the feedback information from all the users on the TV program; based on an allowable advertisement set for the current time slot, an advertisement auto-matching element computes the correlation between each specific object and each advertisement of the allowable advertisement set, computes a score for each advertisement of the allowable advertisement set, and extracts an advertisement candidate group for broadcasting.