Content Acquisition Simulator for Streaming Optimization
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Solution Overview
Problem
Existing content acquisition strategies struggle to accurately predict the incremental impact of new content items on existing content portfolios in media environments, as they fail to account for viewer interest distribution and overlap with existing content, leading to suboptimal streaming time and reach.
Innovation Solution
A content acquisition system that includes a simulator to estimate the impact function value of potential content items using event log data, combined with an optimization model to recommend content items that maximize streaming time and reach while adhering to budget constraints, utilizing genetic algorithms or iterated set merging algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional content acquisition strategies are used to purchase content items, then content items can be added to the media environment, but the impact of new content items on existing content portfolios cannot be accurately predicted
Solution Approach 1:
The system performs preliminary simulation of viewer behavior and content impact before actual acquisition decisions are made. The content recommendation system simulator predicts streaming time, reach, and engagement metrics for potential content items before they are added to the portfolio, allowing acquisition strategies to be optimized in advance based on predicted performance rather than relying on post-acquisition analysis
Solution Approach 2:
The system implements a feedback loop where event log data from the actual content recommendation system is continuously fed back into the simulator to refine predictions. The simulator uses real performance data to adjust and improve its impact prediction accuracy over time, creating a closed-loop system that learns from actual viewer behavior patterns
2Productivity
If content items are selected without considering viewer interest distribution and overlap, then acquisition process is simple, but streaming time and reach are suboptimal
Solution Approach 1:
The content recommendation system simulator serves multiple functions simultaneously: it predicts streaming time, estimates reach, analyzes viewer interest distribution, and evaluates content overlap with existing portfolio. This multi-functional simulator consolidates what would otherwise require separate analysis systems, achieving high productivity without proportionally increasing system complexity
Solution Approach 2:
The simulator acts as an intermediary layer between the content acquisition process and the actual content recommendation system. It translates complex viewer behavior patterns and content portfolio dynamics into simplified impact metrics that guide acquisition decisions, mediating between raw data and decision-making without requiring direct complex interactions
3Reliability
If content acquisition is based on various strategies without optimization, then budget can be allocated, but the impact on engagement and revenue is not maximized
Solution Approach 1:
The system dynamically adjusts content acquisition strategies based on simulated impact predictions. Rather than using static acquisition rules, the system continuously evaluates potential content items against current portfolio composition and viewer behavior patterns, adapting acquisition decisions in real-time to maximize engagement and revenue outcomes while maintaining budget constraints
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 a content acquisition system to recommend for acquisition a subset of content items selected from a set of content items available for purchase in relation to a content recommendation system currently used in a media environment. The content acquisition system may include a content recommendation system simulator to estimate an impact function value for a potential subset of content items of the set of content items available for purchase based on the currently used content recommendation system. Afterwards, an acquisition recommender can recommend for acquisition a subset of content items based on an optimized objective function value calculated based on an optimization model while meeting one or more budget constraints.