Media Consumption Aggregation via Pattern Extraction
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Solution Overview
Problem
Existing media consumption tracking systems fail to accurately aggregate consumption data from various sources, such as movie theaters and social networks, leading to incomplete user consumption histories and inadequate personalized recommendations.
Innovation Solution
A networked environment with a consumption aggregator, solicitor, and recommendation engine that records media consumption through user interactions like ratings, reviews, check-ins, and explicit indications, and uses these data to generate consumption histories and personalized recommendations based on consumption patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If media consumption data is aggregated from multiple sources (movie theaters, social networks, ratings, reviews), then the completeness and accuracy of user consumption history is improved, but the complexity of the data aggregation system increases
Solution Approach 1:
The system divides the aggregation process into separate functional modules: a consumption data receiver that collects data from multiple sources, a consumption history updater that processes and stores the data, and a pattern identifier that analyzes consumption patterns. This segmentation allows each module to handle specific tasks independently, reducing overall system complexity while maintaining high accuracy in consumption history tracking.
Solution Approach 2:
The patent introduces a consumption aggregation system as an intermediary layer between diverse data sources (movie theaters, social networks, rating platforms) and the user profile database. This intermediary standardizes and normalizes data from different sources before storing it in the consumption history, enabling accurate aggregation without requiring direct integration between all source systems.
2Adaptability or versatility
If the system tracks and analyzes detailed consumption patterns across diverse platforms, then personalized recommendations are improved, but the amount of data processing and storage requirements increase
Solution Approach 1:
The system extracts only the essential consumption pattern information needed for personalization, such as media types consumed, frequency of consumption, and preferred categories. Rather than storing and processing all raw data from multiple sources, the system extracts key patterns and stores them in the user profile, reducing data processing volume while maintaining personalization capability.
Solution Approach 2:
The consumption pattern identifier continuously analyzes and pre-processes consumption data in the background, identifying patterns and updating user profiles before recommendation requests are made. This preliminary action ensures that when personalized recommendations are needed, the system can quickly generate them using pre-analyzed patterns rather than processing raw data in real-time.
3Ease of operation
If the system dynamically updates consumption history based on user interactions, then user engagement is improved, but the real-time processing requirements increase
Solution Approach 1:
The system implements continuous background processing that constantly monitors user interactions and updates consumption history in real-time. The consumption data receiver continuously receives data from multiple sources, and the consumption history updater continuously processes this data, ensuring that the user profile is always current without requiring intensive batch processing operations.
Data Source
AI summary
Disclosed are various embodiments for dynamically determining media consumption of a user. A user may perform at least one of a plurality of consumption indication events for a media item. The consumption indication events may include submitting a rating of the media item, submitting a review of the media item, indicating a present consumption of the media item, indicating a past consumption of the media item, etc. It may be determined that the user has consumed the media item in response to determining that the user has performed at least one of the consumption indication events for the media item.