Popularity Algorithm for Detecting Underplayed Music Items
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Solution Overview
Problem
Existing systems fail to accurately determine underplayed or overplayed items across different music playback and purchasing platforms, leading to discrepancies in popularity data and a lack of authentic music recommendations.
Innovation Solution
A popularity algorithm is developed to track discrepancies in primary and secondary sources, such as radio play data and music identification services, to identify underplayed or overplayed items by calculating virtual radio plays per day on one million stations and normalizing music identification data, resulting in sorted lists of underplayed or overplayed items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If radio play data and music identification service data are used as primary and secondary sources, then music recommendations can be provided, but discrepancies in popularity data occur between different platforms
Solution Approach 1:
The system continuously compares radio play data with music identification service data to detect discrepancies in popularity measurements. This feedback loop allows the system to identify when one source diverges from the other and adjust recommendations accordingly, resolving the contradiction between providing versatile recommendations and maintaining measurement precision.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes both radio play data and music identification service data separately before comparing them. This intermediary layer normalizes and prepares the data from both sources, enabling accurate discrepancy detection without letting the conflicting measurements directly undermine each other's validity.
2Reliability
If popularity data from multiple sources are tracked, then underplayed and overplayed items can be identified, but system complexity increases
Solution Approach 1:
The system segments the popularity determination process into distinct modules: one for processing radio play data, another for processing music identification service data, and a third for comparing the two sources. This segmentation allows complex multi-source tracking to be managed through modular, independent components that can be developed and maintained separately.
Solution Approach 2:
The patent transforms the comparison of popularity data by changing parameters such as normalizing play counts to per-capita or per-station averages, and converting different measurement scales into comparable units. This parameter transformation simplifies the comparison process and reduces the apparent complexity of tracking multiple data sources.
Data Source
AI summary
Systems and methods for determining underplayed or overplayed items are provided herein. Instructions stored in memory are executed by a processor to: calculate short term scores for radio data and music identification service data of the items, determine linear distance and logarithmic distance between radio virtual radio plays per days on one million stations (VRPDOMS) and identification VRPDOMS of the items, identify and eliminate items lacking minimum requisite number of radio VRPDOMS or identification VRPDOMS, and sort the items by largest linear distance or largest logarithmic distance first. The sorted items are then transmitted to the computing device.