Feedback Prediction Model for Content Stream Timing
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Solution Overview
Problem
Providers of digital content streams face challenges in determining when to introduce new content items to users, as they often lack feedback, making it difficult to predict user enjoyment and potentially affecting the quality of the content stream.
Innovation Solution
A computer-implemented method that collects feedback from users with associated time data, generates a feedback model to predict when users are likely to provide feedback, and selects content items for insertion into the stream at optimal times to increase feedback likelihood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If new content items are introduced into the content stream, then the content variety and user engagement are improved, but the ability to predict user enjoyment and receive feedback deteriorates due to lack of feedback data
Solution Approach 1:
The system performs preliminary actions by introducing new content items into the content stream before sufficient feedback data is available. The feedback prediction model is trained on feedback from other users to predict whether the current user will enjoy the new content item, enabling the system to proactively curate content even in the absence of user-specific feedback history.
Solution Approach 2:
The feedback prediction model acts as an intermediary between the lack of user-specific feedback data and the need for accurate content recommendations. By leveraging feedback from other users as a proxy, the model bridges the gap between limited data availability and the requirement for reliable predictions about user enjoyment.
2Adaptability or versatility
If new content items without sufficient feedback are provided to users, then content diversity is improved, but the quality of the content stream deteriorates due to potential mismatch with user preferences
Solution Approach 1:
The system implements a feedback mechanism where user responses to new content items are collected and used to train the feedback prediction model. This feedback loop enables the system to learn from actual user preferences over time, improving the accuracy of predictions and ensuring that content diversity does not compromise content quality.
Solution Approach 2:
The system dynamically adjusts the parameters of content recommendation by changing from relying solely on user-specific feedback to incorporating feedback from other users. This parameter change allows the system to maintain content quality standards while introducing diverse new content items that would otherwise be too risky to recommend.
3Measurement precision
If feedback collection is emphasized for new content items, then prediction accuracy is improved, but the timing flexibility and user experience deteriorate due to delayed content delivery
Solution Approach 1:
The system performs preliminary training of the feedback prediction model using feedback from other users before presenting new content items to the current user. This preliminary action enables the system to have prediction capabilities ready in advance, eliminating delays that would occur if feedback collection had to wait for actual user interactions with each new content item.
Solution Approach 2:
The system uses feedback from a subset of users (other users who have provided feedback on similar content) rather than waiting for complete feedback from all users. This partial action approach allows the model to be trained sufficiently for prediction purposes without requiring exhaustive feedback collection, thereby reducing content delivery delays.
Data Source
AI summary
A content server system provides content items to a plurality of users in a content stream. The content server system provides an interface to collect feedback from the plurality of users on the content items in the content stream. Based on the collected feedback the content server system generates a feedback model that predicts the likelihood of receiving feedback on a content item at any given time in a streaming session. The content server system then selects a content item in need of feedback and, using the model, determines a time at which the content item is more likely to receive feedback. The content server system then provides the content item to the user at the determined time.