Content Recommendation Engine Location Device Context
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Solution Overview
Problem
Current content recommendation techniques fail to account for various factors influencing consumer content buying decisions, particularly when consumers switch devices for content consumption, leading to missed opportunities for providers to recommend a wider variety of content and generate greater revenue.
Innovation Solution
A method that recommends content to users based on their location and available devices, creating user profiles that include content, location, and device profiles, and using a recommendation engine to suggest content matching these attributes, even if the consumption device differs from the request device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content recommendations are based on the device through which the consumer makes a request, then the recommendation accuracy for that specific device is improved, but the variety of recommendable content is limited when consumers use different devices for consumption
Solution Approach 1:
The patent segments the recommendation process into device-specific capability assessment and user-specific preference matching. The system separates device capability profiling from user content preferences, allowing recommendations to be tailored to both the requesting device and the user's actual consumption device, thereby resolving the contradiction between accuracy and variety.
Solution Approach 2:
The patent adds a new dimension to content recommendations by incorporating location-based device availability detection. Instead of solely relying on the requesting device, the system detects what devices are available at the user's current location and adjusts recommendations accordingly, expanding the recommendation space beyond device-type matching to include contextual device availability.
2Device complexity
If content recommendations assume the same device is used for both requesting and consuming content, then the recommendation process is simplified, but revenue opportunities are lost when consumers switch devices
Solution Approach 1:
The patent applies preliminary action by detecting available devices at the user's location before making content recommendations. The system proactively identifies what devices the user has access to at their current location and prepares recommendations accordingly, rather than waiting for the user to manually specify their consumption device. This advance detection enables the system to optimize recommendations for the actual consumption context.
Solution Approach 2:
The system implements feedback loops where user content consumption patterns across multiple devices are continuously monitored and used to refine future recommendations. The patent tracks which devices users actually use for consumption and feeds this information back into the recommendation engine, improving both accuracy and revenue opportunities over time without significantly increasing user-facing complexity.
3Ease of operation
If content recommendations consider multiple factors including location and device availability, then user satisfaction is improved, but the system complexity increases
Solution Approach 1:
The patent implements self-service by automatically detecting the user's location and available devices without requiring manual input. The system autonomously queries location services and device availability, then seamlessly integrates this information into the recommendation process. This automation delivers personalized, context-aware recommendations while minimizing the operational burden on users.
Data Source
AI summary
A method for making a content recommendation to at least one user commences by first establishing a location for the at least one user. Thereafter a check occurs to determine which devices are available to the at least one user to use (e.g., consume) the content at the location. Next, the content available to the at least one user is determined based on at least the user's location and available devices available to the at least one user. A content recommendation is then made among the available content based on at least one attribute of the at least one user.