Media Guidance Application Contextual Activity Matching
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Solution Overview
Problem
Existing media recommendation systems often provide irrelevant or annoying suggestions to users by not considering the temporal and contextual relevance of their activities in relation to the media they are consuming.
Innovation Solution
A media guidance application that identifies user-planned activities, filters upcoming events, and matches their characteristics with the context of the media being consumed, generating relevant recommendations based on temporal and contextual relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If recommendations are provided based on user activities without considering media context, then user engagement may be improved through personalized suggestions, but relevance to current media consumption deteriorates causing recommendations to be ignored or perceived as annoying
Solution Approach 1:
The system continuously monitors user media consumption and provides feedback by adjusting recommendations in real-time based on the nexus between user activities and current media content, ensuring recommendations remain relevant while personalized
Solution Approach 2:
The system pre-identifies user activities and their temporal contexts before media consumption occurs, allowing recommendations to be prepared and delivered at the optimal moment when they are most relevant to both user interests and media content
2Adaptability or versatility
If recommendations are provided without temporal filtering of activities, then more user interests can be covered, but recommendations become less timely and relevant to upcoming activities
Solution Approach 1:
The system performs preliminary identification and filtering of user activities within a threshold period before the present moment, preparing recommendation candidates in advance while ensuring they are temporally relevant to upcoming user activities
Solution Approach 2:
The system dynamically adjusts the threshold period based on the type of activity being considered, allowing flexible temporal coverage that adapts to different activity contexts while maintaining timeliness of recommendations
3Loss of information
If multiple non-media guidance applications are queried to identify user activities, then completeness of activity information is improved, but system complexity and querying time increase
Solution Approach 1:
The media guidance application serves multiple functions by integrating activity identification across diverse non-media guidance applications, using a unified approach to query and synthesize activity information from multiple sources without requiring separate handling for each application type
Data Source
AI summary
Systems and methods are provided herein for displaying recommendations that are temporally relevant to activities of a user and are contextually relevant to a portion of viewed media. This may be accomplished by a media guidance application identifying activities a user has planned, as well as respective times for which each of the plurality of activities was planned to be performed by the user. The media guidance application may filter the activities by determining which of the first plurality of activities has a corresponding respective time that is within a threshold period of time. The media guidance application may then identify characteristics of each filtered activity and of a media asset that the user is consuming at the present moment, and may compute whether the characteristics match. If the characteristics match, the media guidance application may generate for display a recommendation relating to the respective activity.