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Output details

11 - Computer Science and Informatics

Robert Gordon University

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Output 40 of 72 in the submission

Output title

Finding the Hidden Gems: Recommending Untagged Music

Type

E - Conference contribution

DOI

10.5591/978-1-57735-516-8/IJCAI11-376

Name of conference/published proceedings

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJACAI 2011)

Volume number

Issue number

First page of article

2256

ISSN of proceedings

1045-0823

Year of publication

2011

URL

Number of additional authors

Additional information

This paper was accepted for oral presentation at this world-leading AI conference (17.1% acceptance [IJCAI Preface]). Although the paper’s topic is music recommendation, its significance comes from its evaluation method that uses social media as a proxy for a user trial. Its novel Association Score combines statistical data on listening and liking tracks available in last.fm to estimate the quality of recommendations. Subsequently, Association Score has been compared with popular proxies (genre, year, cultural similarity) against a real user study for music recommendation; association score shows the best correlation with relevance from real users [Horsburgh’s PhD Thesis http://hdl.handle.net/10059/859].

Interdisciplinary

Cross-referral requested

Research group

None

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract