Content Feed Personalization via Diversity Enforcement
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Solution Overview
Problem
Existing content management systems struggle to effectively select and prioritize relevant items from diverse feeds for users within organizations, as they often get overwhelmed by noisy feeds and lack personalization, leading to dominance of high-traffic sources and reduced visibility of valuable but low-traffic content.
Innovation Solution
A content management system that uses classifiers trained by user interactions to select and prioritize relevant items, incorporating collaborative training, implicit feedback, and diversity enforcement to ensure that all sources are represented, and provides personalized feeds based on individual user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system prioritizes high-traffic sources to ensure popular content visibility, then user engagement with trending content improves, but valuable low-traffic content becomes invisible and diverse sources are underrepresented
Solution Approach 1:
The system dynamically adjusts the visibility parameters of content based on multiple factors including source diversity metrics. Rather than using a single traffic-based parameter, the system modifies content prioritization parameters to balance popularity with source representation, ensuring that low-traffic valuable content receives appropriate visibility while maintaining overall diversity
Solution Approach 2:
The system implements feedback mechanisms that monitor both user engagement metrics and source diversity metrics. This feedback loop allows the system to detect when high-traffic sources dominate the feed and automatically adjust content selection to restore balance, preventing any single source from monopolizing visibility while maintaining engagement
2Quantity of substance
If the system presents all available feed content to users, then comprehensive information coverage is achieved, but users become overwhelmed by noise and relevant content is lost
Solution Approach 1:
The system extracts and removes noise from the feed content through filtering mechanisms that identify and eliminate irrelevant, duplicate, or low-quality items. This extraction process preserves comprehensive information coverage by retaining valuable content while discarding noise that would overwhelm users
Solution Approach 2:
The system segments the feed content into distinct categories and prioritizes them based on relevance to user interests and organizational context. By organizing content into structured segments rather than presenting a flat list, the system maintains comprehensive coverage while making it easier for users to locate relevant information
3Device complexity
If the system uses generic content selection to simplify the algorithm, then system complexity is reduced, but personalized user interests and behaviors cannot be effectively captured
Solution Approach 1:
The system performs preliminary actions by pre-processing and analyzing user interaction data to build personalized profiles before content selection occurs. This preliminary personalization work is done in advance, allowing the main content selection algorithm to operate efficiently while still delivering personalized results through pre-computed user interest models
Data Source
AI summary
Systems and methods for selecting items of interest for an organization from a set of feeds, based on the interests that users have demonstrated through their interactions with existing content, are described herein. In some embodiments, the system is part of a content management service that allows users to add and organize files, media, links, and other information. The content can be uploaded from a computer, imported from cloud file systems, added via links, or pulled from various kinds of feeds.