T-RECSYS: A Novel Music Recommendation System Using Deep Learning

被引：40

作者：

Fessahaye, Ferdos ^{[1
]}

Perez, Luis ^{[1
]}

Zhan, Tiffany ^{[1
]}

Zhang, Raymond ^{[1
]}

Fossier, Calais ^{[1
]}

Markarian, Robyn ^{[1
]}

Chiu, Carter ^{[1
]}

Zhan, Justin ^{[1
]}

Gewali, Laxmi ^{[1
]}

Oh, Paul ^{[1
]}

机构：

[1] Univ Nevada Vegas, Las Vegas, NV USA

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE) | 2019年

基金：

美国国家科学基金会;

关键词：

music recommendation; deep learning; content-based filtering; collaborative filtering; NETWORKS;

D O I：

10.1109/icce.2019.8662028

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

A recommendation system is a program that utilizes techniques to suggest to a user items that they would likely prefer. This paper focuses on an approach to improving music recommendation systems, although the proposed solution could be applied to many different platforms and domains, including Youtube (videos), Netflix (movies), Amazon (shopping), etc. Current systems lack adequate efficiency once more variables are introduced. Our algorithm, Tunes Recommendation System (T-RECSYS), uses a hybrid of content-based and collaborative filtering as input to a deep learning classification model to produce an accurate recommendation system with real-time prediction. We apply our approach to data obtained from the Spotify Recsys Challenge, attaining precision scores as high as 88% at a balanced discrimination threshold.

引用

页数：6

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