News Recommendation Using Latent Topic Models and Temporal Weighting
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Solution Overview
Problem
Users face challenges in finding relevant news content due to information overload, as existing recommendation systems lack explicit user feedback and struggle to accurately infer user interests in the dynamic and real-time nature of online news, relying heavily on click behavior which does not provide clear interest levels.
Innovation Solution
The implementation of K-Nearest-Neighbor (KNN) based temporal and tag-based models that incorporate user-tag information to recommend news articles, using latent topic models to extract user profiles and quantify article lifetime for personalized content delivery, with a focus on ranking articles based on user click behavior and temporal weighting functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If recommendation systems rely on click behavior to infer user interests, then the system can operate without explicit user feedback, but the accuracy of inferring user interest levels is insufficient
Solution Approach 1:
The patent introduces user tags as an intermediary element that bridges the gap between implicit click behavior and explicit user interests. Tags serve as mediators that capture user preferences more accurately than clicks alone, allowing the system to maintain automatic operation while improving interest inference precision through the intermediate tagging layer
Solution Approach 2:
The system transforms the single-dimensional click data into multi-dimensional user profiles by introducing tag parameters. This parameter expansion allows the system to capture nuanced user interests across multiple dimensions (topics, entities, concepts) rather than relying on a single click metric, thereby improving measurement precision while maintaining automated operation
2Quantity of substance
If news content is published in vast amounts to deliver all important events fast, then the completeness of news coverage is improved, but information overload makes it difficult for users to find relevant content
Solution Approach 1:
The patent segments the vast news corpus into organized collections based on user tags and preferences. Instead of presenting all news content uniformly, the system divides content into personalized segments that match user interests, making it easier for users to find relevant information without reducing the overall quantity of news published
Solution Approach 2:
The system applies local quality by tailoring the news presentation to each user's specific interests and preferences. Each user receives a customized news experience with content prioritized and organized according to their personal tag profile, thereby improving ease of finding relevant content while maintaining comprehensive news coverage
3Measurement precision
If the recommendation system incorporates user tags and temporal information, then the personalization and recommendation quality are improved, but the system complexity increases
Solution Approach 1:
The system performs preliminary action by pre-computing user tags and article metadata before the actual recommendation process. User profiles are built in advance based on click behavior and tag associations, and article collections are pre-organized by topic and relevance. This preliminary processing reduces the complexity of real-time recommendations while maintaining high personalization quality
Data Source
AI summary
Computer systems, methods and computer readable media storing instructions, for providing news item recommendations are disclosed. An example system includes one or more digital memories having stored therein metadata for a plurality of news items and click data corresponding to user interactions with the plurality of news items, and a processor. The processor is configured to: determine a user profile for a user, the user profile including indications of news items previously clicked on by the user; select candidate news items for recommendation from (a) said news items based upon respective similarity distances to news items included in the user profile or (b) news items included in other user profiles that are identified based upon their respective similarity distances to the user profiles; and score the candidate news items using, at least in part, a temporal aspect of the candidate news items.