Contextual Media Recommendation via Referral Source Analysis
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Solution Overview
Problem
The sheer quantity of media content available online makes it challenging for online services to effectively target and recommend relevant related content to users, as different attributes of consumed media may have varying degrees of usefulness depending on the user context.
Innovation Solution
A system and method that determine contextual data associated with a user's media content request, using a referral source identification component and a determination component to analyze context data, and a contextual suggestion component to recommend additional content based on this data, incorporating factors like referral source, consumption characteristics, and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If online services use traditional attribute-based methods to identify related content, then the system implementation is simple, but the relevance of suggested content decreases due to ignoring user context
Solution Approach 1:
The system segments the content recommendation process into multiple independent components: a referral source identification component that determines the source of the content request, a determination component that identifies context data based on the referral source, and a contextual suggestion component that selects additional content based on the determined context. This segmentation allows each component to specialize in a specific aspect of context analysis, improving overall recommendation accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent introduces a new dimension of analysis by identifying and utilizing referral source information as an additional context parameter. Instead of only analyzing traditional content attributes, the system now considers the referral source (e.g., search engine, social media, email) as a critical dimension for determining user context. This dimensional expansion enables the system to differentiate between users who encountered the same content through different channels, thereby improving content relevance accuracy.
2Measurement precision
If the system analyzes multiple context attributes to improve recommendation accuracy, then content relevance improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-identifying and storing referral source information when a user requests content. The referral source identification component captures this data upfront, before the actual content recommendation process begins. This preliminary capture of context data eliminates the need for real-time analysis of user behavior patterns during the recommendation phase, thereby reducing processing time while maintaining high recommendation accuracy through the use of pre-gathered contextual information.
3Adaptability or versatility
If the system considers user context and referral source to improve content targeting, then the usefulness of suggested content increases, but the device complexity increases
Solution Approach 1:
The determination component is designed with multi-functionality, serving multiple purposes within the system. It not only identifies context data based on referral sources but also integrates this context information with user profiles and content metadata to generate comprehensive recommendation criteria. This universal component handles both context determination and recommendation generation, reducing the need for separate specialized modules and thereby limiting the increase in system architecture complexity while maintaining high adaptability to user context.
Data Source
AI summary
This disclosure relates to contextual determination of related media content. A referral source identification component determines a referral source associated with a request for media content, and a determination component determines a set of context data for the request based in part on the referral source. A contextual suggestion component identifies or selects a set of additional content based in part on a subset of the context data, and suggests or recommends a subset of the additional content to a user based on a set of recommendation criteria.