Media Consumption Query Response Using Entity History Matching
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Solution Overview
Problem
Existing systems fail to effectively utilize user's media consumption history to provide relevant information in response to queries about entities associated with consumed content.
Innovation Solution
A system that identifies and stores media consumption history, allowing it to provide information about entities and related content based on user queries, using environmental data and media consumption databases to determine consumption times and locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system stores detailed media consumption history including time and location data, then the personalization accuracy of query responses is improved, but the data storage requirements and system complexity increase
Solution Approach 1:
The system segments media consumption data into distinct components: content identifiers, entity identifiers, consumption timestamps, and location data. This segmentation allows the system to store and process only the specific data elements needed for personalization, reducing overall storage requirements while maintaining accuracy. The segmented data structure enables efficient querying and retrieval of specific consumption patterns without processing entire datasets.
Solution Approach 2:
The system performs preliminary processing of media consumption data by pre-identifying and storing entity identifiers (such as actors, directors, producers) alongside content identifiers. This preliminary action prepares the data in advance for rapid retrieval and matching during query processing, eliminating the need for complex real-time analysis and reducing system complexity during actual query operations.
2Measurement precision
If the system processes and analyzes user media consumption history in real-time, then the relevance of query responses is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of media consumption patterns by pre-processing consumption history data to identify and store structured relationships between users, content, and entities. This includes pre-computing consumption frequencies, temporal patterns, and entity associations. When a query is received, the system retrieves these pre-computed results rather than analyzing raw data in real-time, significantly reducing processing time while maintaining high response relevance.
Solution Approach 2:
The system creates simplified copies or representations of complex consumption patterns, such as aggregated statistics and pre-computed entity associations. These copies serve as lightweight data structures that can be rapidly processed and compared during query operations, reducing computational resources and processing time while preserving the essential information needed for relevant responses.
3Loss of information
If the system integrates multiple data sources to build comprehensive media consumption history, then the completeness of user profile information is improved, but the data integration complexity and error handling requirements increase
Solution Approach 1:
The system implements a universal data ingestion framework that can process and integrate consumption data from multiple sources (different media platforms, devices, and formats) through a single standardized interface. This multi-functional approach consolidates what would otherwise require separate integration processes for each data source, reducing overall integration complexity while maintaining comprehensive profile completeness.
Solution Approach 2:
The system introduces intermediary processing layers that standardize and validate data from various sources before integrating it into the consumption history. These intermediaries handle format conversion, data validation, and conflict resolution, isolating the core integration logic from source-specific complexities and error handling requirements.
Data Source
AI summary
Methods, systems, and apparatus for receiving a natural language query of a user, and environmental data, identifying a media item based on the environmental data, determining an entity type based on the natural language query, selecting an entity associated with the media item that matches the entity type, selecting, from a media consumption database that identifies media items that have been indicated as consumed by the user, one or more media items that have been indicated as consumed by the user and that are associated with the selected entity, and providing a response to the query based on selecting the one or more media items that have been indicated as consumed by the user and that are associated with the selected entity.