Content Feed Ordering by Complexity and Time
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Solution Overview
Problem
Content systems face challenges in maximizing content consumption propensity due to variations in content complexity and consumption time, leading to user disengagement when users are presented with inappropriate content lengths and types based on their context, such as device and time of access.
Innovation Solution
A system that selects and orders content based on user profiles, seasonality, and reading habits, determining content complexity and consumption time to match user preferences, using a content selector, ranking service, and tracking server to deliver tailored content feeds that align with user behavior and device usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the content feed presents complex long articles to users, then the content depth and quality are improved, but the user engagement deteriorates when users are accessing from mobile devices during short time periods
Solution Approach 1:
The system dynamically adjusts content characteristics based on real-time context including device type, time of access, and user behavior patterns. Content complexity, length, and formatting are modified adaptively to match the user's current situation, resolving the contradiction between providing deep content and maintaining engagement.
Solution Approach 2:
The system changes multiple content parameters simultaneously including length, complexity, formatting, and media type based on contextual factors. This allows the content to be optimized for either deep reading or quick consumption depending on the user's situation, addressing both content depth and engagement requirements.
2Ease of operation
If the content feed presents short simple stories to users, then the user engagement is improved, but the content depth and quality deteriorate
Solution Approach 1:
The system dynamically adjusts content characteristics based on real-time context including device type, time of access, and user behavior patterns. When conditions favor deep reading (desktop, adequate time), the system presents complex long-form content, thereby maintaining content depth while preserving user engagement.
Solution Approach 2:
The system changes multiple content parameters simultaneously including length, complexity, formatting, and media type based on contextual factors. This allows the content to be optimized for either deep reading or quick consumption depending on the user's situation, addressing both content depth and engagement requirements.
3Ease of operation
If the content feed is customized to match user preferences, then the user engagement is improved, but the system complexity increases
Solution Approach 1:
The system automatically analyzes user behavior patterns, device characteristics, and contextual information to autonomously determine optimal content characteristics. This self-service approach eliminates the need for manual user configuration while achieving high personalization, thereby maintaining engagement without proportionally increasing system complexity.
Solution Approach 2:
The system continuously monitors user interactions and feedback to refine its content selection and customization algorithms. This feedback loop enables the system to improve engagement over time while learning from actual user behavior, making the complexity investment more effective.
Data Source
AI summary
The disclosed embodiments provide a system for maximizing the propensity of content consumption according to content complexity and content consumption time. During operation, the system receives a request from a user to access a content feed. Based on a time of receipt, the system identifies a current seasonality that corresponds to reading habits of the user during a time period encompassing the time of receipt. The system then accesses a profile for the user to obtain a reading speed of the user and the reading habits of the user for the current seasonality. Next, the system: identifies a subset of content items; estimates a reading session length; and determines a complexity, with regard to the user, for each content item. The system then creates the content feed by ordering the subset of content items according to the reading session length and the complexity of each content item.