Location-Based Media Recommendation Timing
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Solution Overview
Problem
Existing media promotion methods, such as email and text messages, are ineffective in motivating users to view media content as they are often received at inconvenient times and locations, limiting their impact on user engagement.
Innovation Solution
A computing system determines a user's content-viewing location based on their location history and identifies a media output device at that location, then provides timely media recommendations when the user is in proximity to the device, considering whether the device is operational and displaying content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If media recommendations are sent via email or text message, then users can be notified of recommended content, but the notifications are received at inconvenient times and locations, reducing their effectiveness
Solution Approach 1:
The system performs preliminary actions by determining the user's content-viewing location in advance using location history data, and then monitors when the user actually arrives at that location to send the recommendation notification at the optimal moment, rather than sending it at a predetermined time
Solution Approach 2:
The system uses location history data as feedback to continuously learn and update the user's content-viewing location patterns, and uses real-time location data as feedback to determine when the user has arrived at the predicted location, enabling dynamic timing of notifications
2Productivity
If media recommendations are sent at prime time, then they may reach more users, but they still do not reach users at the specific moment and location where the user is most interested in viewing
Solution Approach 1:
The system applies local quality by tailoring the notification delivery to the specific location where the user views content, rather than using a general approach for all users. The recommendation is delivered at the local context of the user's actual viewing environment
Solution Approach 2:
The system implements dynamics by making the notification timing adaptive rather than static. The delivery time dynamically adjusts based on the user's real-time location and the predicted content-viewing location, allowing the system to respond to changing user behavior patterns
3Reliability
If the system monitors user location continuously to provide timely recommendations, then recommendation timing improves, but device complexity and energy consumption increase
Solution Approach 1:
The system performs preliminary action by pre-determining the content-viewing location using historical location data before actual use. This allows the system to compare real-time location against a pre-established target location, simplifying the monitoring process
Solution Approach 2:
The system applies partial action by monitoring only for the specific condition of whether the user has arrived at the predicted content-viewing location, rather than continuously analyzing all location parameters. This selective monitoring reduces computational complexity
Data Source
AI summary
A computing system is described that determines, based on a location history, a content-viewing location associated with a user of a mobile computing device and identifies a media output device located at the content-viewing location. Responsive to determining that the mobile computing device is located at the content-viewing location and in proximity to the media output device at a current time, the computing system determines, based on a plurality of features of the media output device, various media recommendations for the user at the current time. The plurality of features include an indication of whether the media output device is operating in an on state at the current time. The computing system outputs, for transmission to the mobile computing device, an indication of the various media recommendations.