Personalized News Stream Ranking via User Content Feature Intersection
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Solution Overview
Problem
Existing news personalization methods fail to provide a diverse and engaging news stream due to the rapid growth of news sources and media types, often resulting in repetitive content recommendations based on user preferences without incorporating social connections or dynamic user interests.
Innovation Solution
A method that identifies user features from personal and social activities, extracts content features, and assigns weights to content items based on intersections, ranking them to create a personalized news stream that includes a variety of news types such as articles, videos, and social media posts, leveraging machine learning techniques to enhance relevance and variety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content selection is based on user preferences, then relevance to user interests is improved, but content variety deteriorates
Solution Approach 1:
The patent segments the news stream into different categories (breaking news, top stories, personalized content) and applies different selection strategies to each segment. This allows the system to maintain high relevance in personalized sections while ensuring variety through curated categories, resolving the contradiction between relevance and variety
Solution Approach 2:
The system dynamically adjusts parameters such as the proportion of personalized versus curated content, and modifies weighting factors based on user engagement metrics. This enables the news stream to adapt between relevance and variety based on real-time conditions, resolving the static trade-off
2Adaptability or versatility
If the news stream includes multiple types of media, then content diversity is improved, but system complexity deteriorates
Solution Approach 1:
The patent implements a universal content processing framework that handles multiple media types (text, video, audio, images) through a single standardized interface. The content selection and ranking mechanisms work uniformly across all media types, reducing system complexity while maintaining content diversity
Solution Approach 2:
The system introduces intermediary components such as content abstractors and metadata generators that convert diverse media types into a standardized representation. This intermediary layer simplifies the processing of multiple media types by translating them into a common format for uniform handling
3Adaptability or versatility
If the system processes multiple news sources, then content variety is improved, but processing time deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-processing news content during off-peak hours, generating metadata, summaries, and categorization tags in advance. This pre-computation reduces the processing time required during real-time news stream generation, allowing the system to handle multiple news sources efficiently
Solution Approach 2:
The system applies different processing depths to different news sources based on their reliability and relevance. High-trust sources receive more thorough processing while lower-priority sources undergo lighter processing, optimizing the balance between content variety and processing time
Data Source
AI summary
Methods, systems, and computer programs are presented for providing a personalized news stream to a user. One method includes an operation for identifying user features associated with a user. The user features include personal features and social features. The personal features are based on activities of the user and the profile of the user. The social features are based on information about social connections of the user. The method further includes operations for extracting content features from a corpus of content items, for identifying intersections between user features and content features, and for assigning weights to the content features from the corpus based on the identified intersections. A score for each content item is determined based on the content features and the respective weights of the content items. The content items are then ranked based on the scores. One or more of the ranked content items are displayed.