Geolocation-Based Media Recommendation System
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Solution Overview
Problem
Current technologies lack effective methods to track and analyze user interaction with digital content items across various devices, failing to provide personalized recommendations based on location and user behavior.
Innovation Solution
An architecture that collects and analyzes content access events from multiple devices, using geolocation and venue data to generate recommendations for users, allowing them to access content items relevant to their location and user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user interaction data is collected across multiple devices, then recommendation accuracy is improved, but data complexity and processing requirements increase
Solution Approach 1:
The patent segments user interaction data into device-specific data sets, where each data set contains interactions from a particular device. This segmentation allows the system to process and analyze data from multiple devices independently while maintaining the ability to aggregate insights across devices, thereby improving recommendation accuracy without overwhelming the system with undifferentiated complex data.
Solution Approach 2:
The patent adds the dimension of device identification to user interaction data, creating a multi-dimensional data structure that tracks interactions not only by user and content item but also by specific device. This dimensional expansion enables the system to distinguish between different devices used by the same user, allowing for more nuanced analysis of user behavior patterns across devices while maintaining organized data processing.
2Adaptability or versatility
If geolocation and venue data are integrated into recommendations, then personalization is improved, but information processing requirements increase
Solution Approach 1:
The patent performs preliminary processing of geolocation and venue data by determining the user's current location and identifying the venue in advance of generating recommendations. This preliminary action allows the system to pre-filter and pre-organize content items based on location-relevant criteria, reducing the computational burden during the actual recommendation generation process while maintaining high personalization capability.
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between raw geolocation data and the recommendation engine. This intermediary layer processes location information, determines venue context, and translates it into meaningful filters or weights for content selection, thereby reducing the direct processing overhead on the main recommendation system while preserving the ability to provide location-based personalization.
Data Source
AI summary
Content items, such as e-books, audio files, video files, etc., may be recommended to a user based on the user's presence at a geolocation or venue. Geolocation is the geospatial location of the user, while a venue is a designated area for an activity, such as a concert hall, aircraft, waiting room, etc. The recommendations may be of content items relating to the geolocation or venue, or they may be content items being accessed by others who are, or have been, in approximately the same geolocation or venue.