Media Content Recommendation System Using Temporal Context
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Solution Overview
Problem
Conventional online media services face challenges in recommending media content that is relevant to users, as the current methods do not effectively utilize user context and temporal-based relevancy to personalize content recommendations.
Innovation Solution
A system comprising a relevancy component, classification component, and notification component that determines topics or events related to media content, assigns classifier values based on data associated with the content and user context, and generates notification messages using temporal-based relevancy values to recommend media content to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional media recommendation systems are used, then media content can be recommended to users, but the relevancy of recommended content is insufficient
Solution Approach 1:
The patent implements dynamic recommendation by continuously updating user context information and temporal-based relevancy values. The system adapts to changing user states (location, time, activity) and dynamically recalculates relevancy scores to ensure recommendations remain relevant despite varying conditions, resolving the contradiction between reliability and adaptability.
Solution Approach 2:
The system changes multiple parameters simultaneously including user context parameters (location, time, activity), media content parameters (classifier values, topics, events), and temporal parameters (time decay factors, recency weights). By adjusting these parameters based on real-time data, the system improves recommendation relevancy while adapting to different user situations.
2Reliability
If comprehensive user context and temporal factors are considered, then recommendation relevancy improves, but system complexity increases
Solution Approach 1:
The patent segments the recommendation system into distinct functional modules: user context analysis component, temporal-based relevancy calculation component, media content classification component, and recommendation generation component. Each module handles specific aspects of the complex processing, making the overall system more manageable and maintainable while achieving high recommendation relevancy.
Solution Approach 2:
The system introduces intermediary components including a user context profile that mediates between raw user data and recommendation logic, and a temporal weighting mechanism that mediates between different time-based factors. These intermediaries simplify the complexity by providing structured interfaces between various processing stages.
Data Source
AI summary
A system for identifying and/or recommending relevant media content is provided. The system includes a relevancy component, a classification component and a notification component. The relevancy component determines a topic or an event related to media content and associates the media content with a group of media content based on the topic or the event. The classification component assigns a classifier value to the media content based on data associated with the media content and the group of media content. The notification component generates a notification message associated with the media content for a user based on the classifier value and a temporal-based relevancy value generated as a function of user context.