Location-Based Media Recommendation System Using Peer Group Segmentation
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Solution Overview
Problem
Conventional systems for generating playlists fail to consider peer group influences and geographic location when recommending music, leading to overbroad results that do not accurately reflect a user's tastes.
Innovation Solution
A computer-implemented method and system that receives play histories from multiple users with time and location data, identifies correlated users based on user preferences and seed information, and generates a list of related media items by comparing seed information to these play histories, weighting media items, and ranking them for presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems analyze all user playlists to generate recommendations, then the coverage of music recommendations is broad, but the accuracy of matching user's specific tastes deteriorates
Solution Approach 1:
The patent segments the user base into peer groups based on demographic characteristics (age, gender, location) and analyzes playlists within each segment. This allows the system to maintain broad coverage across all users while achieving precise matching within each peer group by comparing against a smaller, more relevant subset of play histories.
Solution Approach 2:
The patent applies different analysis approaches to different user segments. Instead of treating all users uniformly, the system tailors the recommendation analysis to local peer group characteristics, weighting certain play histories more heavily based on demographic similarity. This improves accuracy for each user's specific taste profile while maintaining overall system versatility.
2Device complexity
If conventional systems generate recommendations based on general user preferences, then the system complexity is low, but the personalization of music recommendations deteriorates
Solution Approach 1:
The patent performs preliminary segmentation of users into peer groups based on demographic characteristics before generating recommendations. By pre-organizing users into segments and pre-calculating relevant play histories for each segment, the system reduces the complexity of real-time personalization while achieving high personalization accuracy when recommendations are generated.
3Measurement precision
If conventional systems consider geographic location data, then the accuracy of location-based recommendations is improved, but the loss of processing time increases
Solution Approach 1:
The patent applies location-based analysis selectively to relevant peer groups rather than processing all user data with full location analysis. By focusing geographic filtering on the identified peer group segments, the system achieves accurate location-based recommendations while minimizing processing time through targeted rather than comprehensive analysis.
Data Source
AI summary
A computer-implemented method and system are provided for generating media recommendations in a media recommendation network. Aspects of the method and system include receiving by a server a plurality of play histories of media items from a plurality of users of devices, wherein at least a portion of the media items are tagged with corresponding time and location data indicating a time and location of play; receiving by the server a media recommendation request from a requester, including receiving seed information indicating a current location of the requester; using at least one of user preferences of the requester and the seed information to identify correlated users from which to search corresponding play histories from among the plurality of play histories; comparing the seed information to the corresponding play histories and generating a list of related media items contained therein; and returning the list of related media items to the requester.