Media Guidance Application Cross-Device Recommendation Sync
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional media guidance systems fail to effectively share data across multiple devices, resulting in inconsistent content recommendations, as data collected on one device is not utilized on another device.
Innovation Solution
A media guidance application that facilitates data sharing between devices by monitoring and syncing content consumption data, allowing recommendations to be tailored based on recent content consumption on one device and provided on another device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is collected and stored on one device, then content recommendations can be tailored for that device, but the data is not used when recommending content on another device
Solution Approach 1:
The patent merges data collected from multiple devices by establishing a central database that receives and stores content consumption data from various devices. The system combines this data with user profile information to create unified user profiles that span multiple devices, enabling consistent recommendations across different devices while resolving the information loss problem.
Solution Approach 2:
The patent introduces a communication interface as an intermediary component that facilitates data exchange between devices and the central database. This intermediary enables seamless data sharing without requiring direct device-to-device communication, solving the cross-device data accessibility issue while maintaining system architecture simplicity.
2Measurement precision
If content recommendations are based on recent consumption data, then relevance to user interests is improved, but the system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-defining time thresholds and weightings for recent consumption data before actual recommendation generation. The system pre-processes and organizes consumption data into structured formats with predetermined time-based categorizations, reducing the computational complexity during real-time recommendation while maintaining high relevance through recent data focus.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the time threshold and data weighting parameters based on user behavior patterns and device types. The system modifies these parameters to optimize the balance between recency and overall consumption history, achieving precise recommendations without requiring overly complex processing systems.
Data Source
AI summary
Methods and systems are disclosed herein for a media guidance application that provides content recommendations based on recent activity. For example, the media guidance application determines that the user has stopped using the first device and is using the second device. In response, the media guidance application retrieves, from memory, a length of time that the user has consumed media on the first device. The media guidance application then determines the time interval when the user was consuming media on the first device. Next, media content is determined that the user consumed on the first device during the time interval. The media guidance application then determines a characteristic of the consumed media content and recommends media content on a second device based on the characteristic.