Cold-Start Podcast Recommendations via Music Preference Mapping
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Solution Overview
Problem
Streaming content providers face difficulties in recommending relevant podcasts to users who have not previously consumed podcasts, due to the lack of historical consumption data, which is common in user-generated content with minimal metadata.
Innovation Solution
A content server generates podcast recommendations based on the user's historical music consumption data by determining music preferences and mapping them to podcast preferences using a podcast genome, allowing for recommendations in 'cold start' situations where no podcast consumption history is available.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If podcast recommendations are generated using traditional methods based on historical consumption data, then recommendation accuracy is improved for users with history, but cold start users cannot receive relevant recommendations due to lack of data
Solution Approach 1:
The patent introduces music consumption data as an intermediary to bridge the gap between users without podcast history and podcast recommendations. By mapping music preferences to podcast genres through a mapping module, the system uses music data as a mediator to generate initial podcast recommendations for cold start users, enabling recommendation capability where none previously existed
Solution Approach 2:
The system changes the input parameters for recommendation generation by switching from podcast consumption history to music consumption history for cold start users. The mapping module transforms music preference parameters into podcast genre parameters, allowing the recommendation system to function with different data types and expand its adaptability to users with no podcast history
2Measurement precision
If the system waits for users to consume podcasts before generating recommendations, then recommendation quality is improved, but user engagement and content discovery are reduced due to delayed personalization
Solution Approach 1:
The patent performs preliminary action by generating podcast recommendations for cold start users immediately upon detecting no podcast consumption history, rather than waiting for users to accumulate podcast listening data. The mapping module proactively creates recommendations based on existing music preferences, enabling immediate personalization and reducing the time loss while maintaining acceptable recommendation quality
3Ease of manufacture
If podcast metadata is minimally populated by user-generated content, then content creation ease is improved, but recommendation system effectiveness deteriorates due to insufficient information
Solution Approach 1:
The patent introduces music preference data as an intermediary information source to compensate for minimal podcast metadata. By mapping music genre preferences to podcast genres, the system recovers lost information about user podcast interests without requiring users to provide detailed podcast metadata or consumption history, thus maintaining ease of content creation while improving recommendation effectiveness
Data Source
AI summary
A system and a method are disclosed for recommending podcasts. In an embodiment, the system detects a podcast request, and responsively determines whether a profile of the user indicates consumption by the user of podcasts. Responsive to determining that the profile does not indicate consumption by the user of podcasts, the system determines music preferences of the user based on data of the profile, and generates a podcast seed based on the music preferences. The system selects a podcast recommendation based on the podcast seed, and provides the podcast recommendation to the user. The system receives a selection of the podcast recommendation by the user, and responsively plays back a podcast corresponding to the podcast recommendation.