Forward Recommendation Simulation for Content Acquisition Impact
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Solution Overview
Problem
Existing content acquisition strategies struggle to predict the impact of new content on an existing portfolio, often failing to identify the most desirable content that a customer base is likely to engage with, leading to suboptimal user experiences.
Innovation Solution
A recommendation system simulator that utilizes machine learning to predict the impact of new content on an existing content portfolio by simulating user interactions and estimating popularity, incorporating unsupervised learning to adapt to changing demographics and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content acquisition is based on performance optimization using predicted popularity, then the selection process becomes automated and efficient, but the prediction accuracy deteriorates due to difficulty in forecasting metrics for competing content
Solution Approach 1:
The system performs preliminary simulation of content portfolio outcomes before actual acquisition decisions are made. By running forward simulations with candidate content additions, the system evaluates potential impacts on user engagement metrics in advance, allowing stakeholders to make informed acquisition decisions without relying on inaccurate popularity predictions alone.
Solution Approach 2:
The system incorporates feedback loops where simulation results feed back into the content acquisition decision-making process. By continuously evaluating simulated outcomes against actual performance data and adjusting acquisition strategies accordingly, the system improves its ability to identify high-impact content while maintaining automated efficiency.
2Device complexity
If traditional content acquisition strategies are used, then the process is simpler, but the ability to identify desirable content that customers will engage with deteriorates
Solution Approach 1:
The forward simulation system acts as an intermediary layer between traditional acquisition strategies and content decisions. It takes simple acquisition criteria as input, simulates complex user interactions and portfolio dynamics, and produces reliable predictions about content desirability, thereby bridging the gap between process simplicity and decision reliability.
Solution Approach 2:
The system creates virtual copies of the content portfolio and user base to run simulations. By copying the essential dynamics of the real system into a simulated environment, it can evaluate multiple acquisition scenarios without affecting actual operations, thereby identifying desirable content with high reliability while keeping the actual acquisition process relatively simple.
3Loss of time
If new content is added to the portfolio without simulation, then the process is faster, but the impact on existing content portfolio and user reach cannot be predicted
Solution Approach 1:
The system performs partial simulations focusing only on the specific impacts relevant to acquisition decisions, rather than comprehensive full-system simulations. By simulating only the necessary aspects (user engagement with new content, impact on existing content, reach metrics), it obtains critical portfolio impact information quickly, reducing the time loss associated with content addition while preventing information loss about portfolio effects.
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for utilizing a content acquisition recommendation system to generating a set of candidate content assets, generate embeddings and popularity score estimates for the set of candidate content assets, aggregate the set of candidate content assets with a set of existing content assets to generate a simulation set of content assets, determine a target set of users for the simulation set of content assets, generate, for at least a portion of the target set of users and based on a trained machine learning model, a result set of recommended content assets, determining an impact of the candidate content assets located in the result set of recommended content assets and generate a proposal for an acquisition of candidate content assets.