Add to ORKGThe truncated singular value decomposition is a widely used methodology in music recommendation for direct similar-item retrieval or embedding musical items for downstream tasks. This paper investigates a curious effect that we show naturally occurring on many recommendation datasets: spiking formations in the embedding space. We first propose a metric to quantify this spiking organization's strength, then mathematically prove its origin tied to underlying communities of items of varying internal popularity. With this new-found theoretical understanding, we finally open the topic with an industrial use case of estimating how music embeddings' top-k similar items will change over time under the addition of data.Comment: Accepted for RecSys 2...
AbstractRecommender systems seek to predict the ‘rating’ or ‘preference’ that user would give to an ...
The collaborative filtering recommendation algorithm is a technique for predicting items that a user...
Nowadays, advanced information and communication technologies ease the access of music pieces. Howev...
A prevalent practice in recommender systems consists in averaging item embeddings to represent users...
With the volume of digital media available today, automatic music recommendation services have prove...
Recommender systems apply machine learning and data mining techniques for filtering unseen informati...
Searching and organizing growing digital music collections requires a computational model of music s...
Music recommender systems can offer users personalized and contextualized recommendation and are the...
Many different versions of the same song are often found in online music or video services. To enabl...
The next generation of music recommendation systems will be increasingly intelligent and likely take...
Personalized recommendation is, according to the user\u27s interest characteristics and purchasing b...
Music catalogs in music streaming services, on-line music shops and private collections become incre...
Wide-scale distribution of music over the Internet has changed the requirements of the consumer who ...
Over the past century, sociocultural and technological developments have fostered the emergence of w...
The rapid expansion of social media in music has provided the field with impressive datasets that of...
AbstractRecommender systems seek to predict the ‘rating’ or ‘preference’ that user would give to an ...
The collaborative filtering recommendation algorithm is a technique for predicting items that a user...
Nowadays, advanced information and communication technologies ease the access of music pieces. Howev...
A prevalent practice in recommender systems consists in averaging item embeddings to represent users...
With the volume of digital media available today, automatic music recommendation services have prove...
Recommender systems apply machine learning and data mining techniques for filtering unseen informati...
Searching and organizing growing digital music collections requires a computational model of music s...
Music recommender systems can offer users personalized and contextualized recommendation and are the...
Many different versions of the same song are often found in online music or video services. To enabl...
The next generation of music recommendation systems will be increasingly intelligent and likely take...
Personalized recommendation is, according to the user\u27s interest characteristics and purchasing b...
Music catalogs in music streaming services, on-line music shops and private collections become incre...
Wide-scale distribution of music over the Internet has changed the requirements of the consumer who ...
Over the past century, sociocultural and technological developments have fostered the emergence of w...
The rapid expansion of social media in music has provided the field with impressive datasets that of...
AbstractRecommender systems seek to predict the ‘rating’ or ‘preference’ that user would give to an ...
The collaborative filtering recommendation algorithm is a technique for predicting items that a user...
Nowadays, advanced information and communication technologies ease the access of music pieces. Howev...