News Recommendation System Combining User and Community Click Data
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Solution Overview
Problem
Existing news recommendation systems fail to accurately predict user interests as they rely solely on popularity or individual selection history, neglecting broader community trends and interests in news items, especially during exceptional events.
Innovation Solution
A system that combines individual user click data with community click data to adjust likelihoods of media item selection, recommending items based on both personal interests and general population trends, using a server system to process and transmit personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If news items are recommended based solely on popularity, then popular items are recommended to users, but the accuracy of predicting user interest deteriorates
Solution Approach 1:
The patent combines multiple recommendation signals (popularity-based and interest-based) into a unified recommendation system. The system merges general popularity data with individual user selection history to generate personalized recommendations, resolving the contradiction by integrating both approaches rather than relying on either alone.
Solution Approach 2:
The system applies different weighting to popularity and user interest signals based on the specific context and user profile. For users with extensive selection history, individual interest signals are weighted higher; for users with limited history, popularity signals are weighted higher, creating locally optimized recommendations that balance accuracy and volume.
2Measurement precision
If news items are recommended based solely on user selection history, then personal interests are captured, but the ability to account for news trends deteriorates
Solution Approach 1:
The recommendation system dynamically adjusts its behavior based on current news trends and user patterns. It continuously learns from new selection data and popularity information, allowing the system to adapt to changing user interests and news trends in real-time, thus maintaining both personalization and trend awareness.
Solution Approach 2:
The system incorporates feedback loops that monitor user selection behavior and news popularity trends. This feedback mechanism allows the system to refine its recommendations by comparing predicted user interests against actual selection patterns and popular news items, continuously improving both personalization accuracy and trend adaptation.
3Measurement precision
If only individual user data is used for recommendations, then personalization is improved, but the incorporation of community trends deteriorates
Solution Approach 1:
The recommendation system serves multiple functions simultaneously: it personalizes recommendations for individual users while also incorporating community-wide trends and popular news items. This multi-functionality allows the system to operate at both the individual and community levels, preventing loss of either type of information.
Solution Approach 2:
The system merges individual user selection history with community popularity data to create a comprehensive recommendation model. By combining these data sources, the system preserves both personal interest patterns and community trends, eliminating the information loss that would occur if only one data source were used.
Data Source
AI summary
Systems and methods for recommending media content items are provided. In some implementations, a method includes, identifying a first set of media items selected by a first plurality of users; causing a second set of media items to be displayed to a second user not included in the first plurality of users in accordance with the first set of media items. The first set of media items and the second set of media items are associated with a same media item category. In some implementations, the method optionally includes, identifying the second set of media items without regard to media content item selection history associated with the second user. In some implementations, the first and second sets of media items are news items.