DeepAI AI Chat
Log In Sign Up

Enhancing Music Features by Knowledge Transfer from User-item Log Data

by   Donmoon Lee, et al.

In this paper, we propose a novel method that exploits music listening log data for general-purpose music feature extraction. Despite the wealth of information available in the log data of user-item interactions, it has been mostly used for collaborative filtering to find similar items or users and was not fully investigated for content-based music applications. We resolve this problem by extending intra-domain knowledge distillation to cross-domain: i.e., by transferring knowledge obtained from the user-item domain to the music content domain. The proposed system first trains the model that estimates log information from the audio contents; then it uses the model to improve other task-specific models. The experiments on various music classification and regression tasks show that the proposed method successfully improves the performances of the task-specific models.


page 1

page 2

page 3

page 4


Deep Content-User Embedding Model for Music Recommendation

Recently deep learning based recommendation systems have been actively e...

Deriving item features relevance from collaborative domain knowledge

An Item based recommender system works by computing a similarity between...

Music Style Classification with Compared Methods in XGB and BPNN

Scientists have used many different classification methods to solve the ...

Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text

Collaborative filtering (CF) is the key technique for recommender system...

Transfer learning for music classification and regression tasks

In this paper, we present a transfer learning approach for music classif...

Recognizing Musical Entities in User-generated Content

Recognizing Musical Entities is important for Music Information Retrieva...

Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation

Recently online advertisers utilize Recommender systems (RSs) for displa...