SeER: An Explainable Deep Learning MIDI-based Hybrid Song Recommender System

06/25/2019
by   Khalil Damak, et al.
0

State of the art music recommender systems mainly rely on either Matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. Despite advances in deep learning for song recommendation, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy, other significant aspects are important, such as explainability and solving the cold start problem. In this work, we propose a hybrid deep learning structure, called "SeER", that uses collaborative filtering (CF) and deep learning sequence models on the MIDI content of songs for recommendation in order to provide more accurate personalized recommendations; solve the item cold start problem; and generate a relevant explanation for a song recommendation. Our evaluation experiments show promising results compared to state of the art baseline and hybrid song recommender systems in terms of ranking evaluation.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/20/2012

Content-boosted Matrix Factorization Techniques for Recommender Systems

Many businesses are using recommender systems for marketing outreach. Re...
research
05/26/2021

A Hybrid Recommender System for Recommending Smartphones to Prospective Customers

Recommender Systems are a subclass of machine learning systems that empl...
research
12/23/2019

An Explainable Autoencoder For Collaborative Filtering Recommendation

Autoencoders are a common building block of Deep Learning architectures,...
research
04/27/2021

A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation

Influenced by the stunning success of deep learning in computer vision a...
research
09/03/2017

Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario

Item cold-start is a classical issue in recommender systems that affects...
research
07/17/2019

Evaluating Recommender System Algorithms for Generating Local Music Playlists

We explore the task of local music recommendation: provide listeners wit...
research
07/25/2021

Content-driven Music Recommendation: Evolution, State of the Art, and Challenges

The music domain is among the most important ones for adopting recommend...

Please sign up or login with your details

Forgot password? Click here to reset