Music Genre Embeddings for Similarity-Based Song Recommendations
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Solution Overview
Problem
Conventional recommender systems struggle to accurately group media content like songs based on qualitative descriptors such as genre, leading to poor user experiences and inefficient use of computing resources due to misidentified genres and inclusion of dissimilar songs.
Innovation Solution
A computer system generates a genre embedding using an attribute similarity engine that quantifies and encodes similarity information between genres, applying a network feature learning algorithm to a graph of genre relationships to filter and rank songs based on similarity, thereby improving the accuracy and efficiency of song recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional recommender systems group songs based on qualitative genre descriptors, then user preference matching is enabled, but measurement precision and reliability of genre classification deteriorate
Solution Approach 1:
The patent transforms the qualitative genre descriptor into a quantitative representation by computing a genre vector from audio features (tempo, energy, danceability, valence, acousticness, instrumentalness, liveness, speechiness). This parameter transformation enables precise mathematical comparison between songs using Euclidean distance, resolving the contradiction between enabling preference matching and maintaining classification accuracy.
2Quantity of substance
If conventional systems include all songs matching user preferences, then completeness of recommendations is improved, but loss of time and computational resources increase due to filtering dissimilar songs
Solution Approach 1:
The patent pre-computes the genre vector for each song in the library and stores it alongside the audio features. This preliminary action enables rapid similarity comparison during recommendation generation without requiring real-time audio analysis, significantly reducing processing time while maintaining completeness of the song collection.
3Productivity
If automated genre labeling is applied to all songs, then productivity of catalog organization is improved, but manufacturing precision of genre assignment deteriorates due to misidentification
Solution Approach 1:
The patent implements a feedback mechanism where user interactions (skips, plays, ratings) are used to refine and update genre vectors over time. The system learns from user behavior to improve the accuracy of genre classification, allowing automated labeling to maintain high precision while preserving productivity benefits.
Solution Approach 2:
The genre vector is not static but dynamically updated based on new audio analysis and user feedback. This dynamic approach allows the system to adapt to evolving music trends and improve classification accuracy over time, resolving the contradiction between automated processing speed and label precision.
Data Source
AI summary
Techniques are disclosed for generating an attribute embedding for a catalog of items. A computer system can determine pairwise relationships between attributes of items in a digital catalog. The computer system can use the pairwise relationships to generate a graph including attribute nodes. Each attribute node can be related to each other attribute node of the graph according to the pairwise relationships. The computer system can also generate an attribute embedding based on the graph. The computer system can then generate a collection of items from the items in the digital catalog using the attribute embedding.