Personalized Post Session Model for Online Newsfeed Content Ranking
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Solution Overview
Problem
Existing methods for presenting content in online newsfeeds fail to effectively consider user interaction attributes, leading to suboptimal selection and ordering of content items that encourage users to post new content.
Innovation Solution
A personalized post session model is implemented using a trained prediction model that evaluates user and content features to determine the likelihood of users posting new content, incorporating interaction scores such as liking, commenting, and sharing to rank and select content items for presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing methods analyze connections of users to select newsfeed content, then content selection is simplified, but user engagement and likelihood of posting new content is reduced
Solution Approach 1:
The patent changes the selection parameters from simple connection-based metrics to a comprehensive scoring system that incorporates multiple attributes including user connection strength, content attributes, user posting history, and interaction likelihood. This transforms the content selection from a simplistic approach to a multi-dimensional evaluation that better predicts user engagement and posting behavior.
Solution Approach 2:
The system implements feedback loops where user interactions with presented content (likes, comments, shares, posting) are continuously monitored and fed back into the scoring model. This allows the system to learn from actual user behavior and refine content selection predictions, improving both user engagement and posting activity over time.
2Ease of manufacture
If existing methods present content based on user connections, then implementation is straightforward, but content relevance to user posting behavior is insufficient
Solution Approach 1:
The patent segments the content selection process into distinct scoring components: connection score, content attribute score, user posting history score, and interaction likelihood score. Each component is calculated separately based on specific attributes, allowing for precise measurement of different factors that contribute to overall content relevance and user posting probability.
Solution Approach 2:
The system creates a composite scoring mechanism that combines multiple independent scoring dimensions into an overall content selection score. This composite approach integrates diverse data sources including user profiles, content attributes, historical behavior, and connection metrics to achieve high precision in predicting content relevance and user posting likelihood.
3Measurement precision
If a comprehensive scoring system is implemented to predict user posting behavior, then content selection accuracy improves, but system complexity increases
Solution Approach 1:
The complex prediction problem is divided into modular scoring components that can be independently calculated and combined. Each score (connection score, content score, history score, interaction score) handles a specific aspect of user behavior prediction, making the overall system more manageable and easier to implement despite the comprehensive nature of the analysis.
Data Source
AI summary
An online system selects a number of content items and presents the selected content items through a feed to a target user, where each selected candidate content item is likely to cause the target user to post his/her new content in response to the selected candidate content item within a short period of time. The online system selects the candidate content items for presentation through the feed using a trained post session prediction model. A ranking score for a candidate content item is determined based on a probability value indicating likelihood that the candidate content item causes the target user to post new content. The probability value is determined by applying a trained model to user features of the target user and content features of the candidate content item. The online system ranks the candidate content items based on their ranking scores and present the feed to the target user.