Media Guidance Application Activity-Based Content Recommendation
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Solution Overview
Problem
Users are often unaware of available media content and struggle to find content that aligns with their current activities, as existing media guidance systems fail to provide personalized recommendations based on recent user activities.
Innovation Solution
A media guidance application that receives activity data from users or devices, cross-references it with a database of media assets accessed during similar activities, and recommends available media assets, considering factors like availability, user equipment, and proximity to ensure timely and suitable content suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If media guidance systems provide comprehensive media content information, then users have more content options, but users still struggle to find content that aligns with their current activities
Solution Approach 1:
The system transitions from providing generic media recommendations to context-specific recommendations by analyzing local activity data (laundry, dishes, cooking) and matching it with relevant media content. The guidance application queries activity data from home appliances to determine what the user is currently doing and recommends media that aligns with that specific local context, making content discovery more intuitive and activity-relevant.
2Adaptability or versatility
If the system analyzes user activities and provides personalized recommendations, then content relevance improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer in the form of a guidance application that sits between the user's media consumption needs and the available content. This intermediary queries home appliances for activity data, processes that information against a database of media content, and delivers personalized recommendations. The intermediary handles the complexity of data collection, processing, and matching, shielding the user from system complexity while delivering tailored content.
3Measurement precision
If the system determines media availability and matches it with user activities, then recommendation accuracy improves, but time required for processing increases
Solution Approach 1:
The system performs preliminary actions by continuously querying home appliances for activity status and pre-processing media content availability information. The guidance application maintains an updated understanding of what media is available and what activities users are currently performing, allowing for rapid matching when recommendations are needed. This preliminary data gathering and processing reduces real-time computation requirements and speeds up recommendation delivery.
Data Source
AI summary
Methods and systems are disclosed herein for a media guidance application that recommends media content based on activities recently performed by a user. For example, in response to determining that a user recently finished the laundry, the media guidance application may recommend a movie that other users accessed after finishing the laundry.