Media Recommendation System Using Location and Peer Data
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Solution Overview
Problem
Conventional systems for generating media playlists fail to consider peer group influences and geographic location when recommending music, often resulting in recommendations that are too broad and not tailored to individual user preferences.
Innovation Solution
A system that tracks media playback history and location, allowing users to define media channels based on geographic areas of interest, user-based criteria, and content-based criteria to provide personalized media recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use broad genre-based metadata analysis for media recommendations, then the system can provide general music suggestions, but the recommendations become too broad and fail to capture individual user preferences and peer group influences
Solution Approach 1:
The patent segments the recommendation system into multiple components: seed media selection, play history analysis, location-based filtering, and peer group identification. Each component processes specific data types independently before integrating results, allowing the system to maintain high recommendation accuracy through multiple filtering stages while managing complexity through modular architecture
Solution Approach 2:
The patent adds new dimensions to the recommendation space by incorporating geographic location data and social peer group relationships alongside traditional metadata analysis. This multi-dimensional approach transforms 2D genre-based recommendations into 4D recommendations spanning genre, user history, location, and social influence, thereby improving precision without linearly increasing complexity
2Measurement precision
If the system incorporates location data and play history analysis to personalize recommendations, then recommendation accuracy improves, but data processing requirements and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing play history data, location information, and media metadata during off-peak periods. User profiles and peer group relationships are pre-computed and stored for rapid retrieval during recommendation generation, significantly reducing real-time processing energy requirements while maintaining high personalization accuracy
Solution Approach 2:
The system implements self-service mechanisms where user devices locally filter and aggregate their own play history data before transmission to the server. Location-based peer groups are automatically formed and maintained based on geographic proximity, reducing the computational burden on central servers and minimizing energy consumption for data processing
Data Source
AI summary
A method for generating a media recommendation is disclosed. The method comprises receiving information about a user associated with a requesting device, identifying profile information based on the information about the user, receiving a media recommendation request from the requesting device, the media recommendation request comprising seed information comprising information identifying a media item, determining at least one related media item based on at least the information identifying the media item and the profile information, and providing information identifying the at least one related media item to the requesting device.