Track Selection System for Balanced Streaming Playlists
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Solution Overview
Problem
Service providers face challenges in generating streaming content playlists that balance familiar and unfamiliar content, leading to user dissatisfaction and potential loss of subscriptions, as users either find familiar content boring or miss it when it's absent.
Innovation Solution
A system and method for selecting a playlist that includes a processor receiving request parameters, loading relevant data from databases, calculating the most recent discovery track, and repeatedly selecting artist identifiers and track types to return a predetermined number of tracks, excluding banned or recently played content, to create a balanced playlist.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the service provider generates a playlist with only familiar content, then the user's desire for familiar content is met, but the user finds the content boring and tires of the streaming service
Solution Approach 1:
The system applies local quality by categorizing tracks into different types (familiar, discovery, mixed) and strategically placing them at specific positions in the playlist. Each position in the playlist has a designated track type that serves a specific purpose: familiar tracks provide comfort, discovery tracks provide novelty, and mixed tracks bridge both experiences. This localized differentiation of content quality throughout the playlist resolves the contradiction between reliability and engagement.
Solution Approach 2:
The system implements periodic action by alternating between familiar tracks and discovery tracks at regular intervals throughout the playlist. Rather than presenting all familiar content first or all new content first, the system periodically introduces discovery tracks among familiar tracks, creating a rhythmic pattern that maintains user engagement while ensuring familiarity. This periodic alternation prevents monotony while maintaining reliability.
2Adaptability or versatility
If the service provider generates a playlist with only unfamiliar content, then the user's desire for new content is met, but the user becomes desirous of familiar content and ceases subscribing
Solution Approach 1:
The system addresses this contradiction by assigning different qualities to different positions in the playlist. Discovery tracks (unfamiliar content) are placed at specific intervals to provide variety and adaptability, while familiar tracks are placed at other positions to ensure reliability and user retention. This localized quality assignment ensures that neither extreme dominates the entire playlist experience.
Solution Approach 2:
The system uses periodic action to balance content variety with user retention by regularly interspersing familiar tracks among discovery tracks. This periodic reinforcement of familiar content ensures that users consistently receive the reliability they need, preventing them from becoming desirous of familiar content to the point of canceling their subscription, while still providing adequate variety through discovery tracks.
3Ease of operation
If the service provider allows users to sort through all available content, then users have complete control over their playlists, but users do not want to sort through all the available content and generate their own playlists
Solution Approach 1:
The system applies self-service by automatically generating playlists based on user preferences and listening history without requiring users to manually sort through content. The system serves itself by using its own algorithms and data to create personalized playlists, freeing users from the time-consuming task of playlist generation while still providing customized results that reflect user tastes.
Solution Approach 2:
The system implements preliminary action by pre-processing user listening history and preferences to pre-categorize tracks into familiar and discovery types before playlist generation. This preliminary classification work is done in advance, allowing the system to quickly assemble balanced playlists without requiring users to spend time evaluating individual tracks, thus reducing the time loss while maintaining user control over the overall playlist structure.
Data Source
AI summary
Methods, apparatuses, and computer-readable products for selecting tracks. A plurality of request parameters are received from a client device. Based on those request parameters, plurality of bans, history track attributes, and artist identifiers are loaded from a database. A most recent discovery track is calculated based on the plurality of histories and the plurality of artist identifiers. An artist identifier is repeatedly selected from the plurality of artist identifiers along with a track type from a set of track types until a predetermined number of artist identifier and track type pairs have been selected. A plurality of candidate tracks for each selected artist identifier are loaded from a database. One track of the plurality of candidate tracks is repeatedly selected for each artist identifier and track type pair until one track has been selected for each pair of the predetermined number of artist identifier and track type pairs. The predetermined number of tracks that have been selected are returned to the client device.