User Behavior Model Generation for Heterogeneous Content Prediction
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Solution Overview
Problem
Current systems fail to accurately predict user reactions to diverse content types, such as news items and social network updates, beyond movie recommendations, due to insufficient data and limited analysis of user behavior across heterogeneous data sources.
Innovation Solution
A system that logs user activities from various data sources, generates a predictive model by expanding user and content attributes, and uses a scoring engine to predict user reactions to new content, incorporating features and weights to score content items based on user interests and behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a DVD rental service analyzes user behavior to suggest movies, then movie recommendations are improved, but the system lacks sufficient information to predict user interest in other content areas such as news items and social network updates
Solution Approach 1:
The patent applies universality by creating a predictive model that functions across multiple content types including news items, social network updates, videos, and articles. The model generation engine processes heterogeneous data from diverse sources to generate unified predictions for different content categories, making the system versatile rather than specialized for a single content type.
Solution Approach 2:
The patent segments user behavior data into distinct categories such as endorsements, dislikes, comments, shares, clicks, and saves. Each segment is analyzed separately by the model generation engine to extract specific behavioral patterns, which are then integrated to form comprehensive predictions across different content types.
2Measurement precision
If the system logs and analyzes user activities from heterogeneous data sources, then prediction capability is improved, but data processing complexity increases
Solution Approach 1:
The patent introduces a model generation engine as an intermediary between raw user activity data and prediction results. This intermediary component processes heterogeneous data from multiple sources, standardizes it into a unified format, and generates predictive models that simplify downstream scoring operations.
Solution Approach 2:
The system implements self-service through automated logging of user activities across multiple data sources. The logging unit automatically captures user behaviors without manual intervention, and the model generation engine automatically processes this data to create predictive models, reducing operational complexity.
3Measurement precision
If the model generation engine expands attributes by content and user characteristics, then prediction accuracy is improved, but computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing user characteristics and content attributes in the model generation phase. This preprocessing step prepares the data structures in advance, so that during the scoring phase, the system can quickly retrieve and apply pre-computed values without intensive real-time computation.
Solution Approach 2:
The patent changes parameters by dynamically adjusting the granularity of attribute expansion based on content type and user profile. The model generation engine selectively expands attributes relevant to specific content categories, avoiding unnecessary computation for attributes that do not contribute to prediction accuracy for particular content types.
Data Source
AI summary
A system and method for generating a model based on the user's interests and activities by receiving with a logging unit user activities from heterogeneous data sources, generating a log of user activities for a content item by joining the user activities for the content item, expanding attributes of the log by at least one of content and by the user to form an expanded log and generating a user model based on the expanded log. A feature extractor extracts features from content items and assigns weights to the features. A scoring engine receives the model and the content items with their associated weighted features and scores the content items based on the user model. The scoring engine generates a stream of content based on the scored content items.