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A Diversity Adjusting Strategy with Personality for Music Recommendation. / Lu, Feng; Tintarev, Nava.

Proceedings of the 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems. ed. / Peter Brusilovsky; Marco de Gemmis; Alexander Felfernig; Pasquale Lops; John O'Donovan; Giovanni Semeraro; Martijn C. Willemsen. CEUR, 2018. p. 7-14 (CEUR Workshop Proceedings; Vol. 2225).

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Lu, F & Tintarev, N 2018, A Diversity Adjusting Strategy with Personality for Music Recommendation. in P Brusilovsky, M de Gemmis, A Felfernig, P Lops, J O'Donovan, G Semeraro & MC Willemsen (eds), Proceedings of the 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems. CEUR Workshop Proceedings, vol. 2225, CEUR, pp. 7-14, IntRS 2018, Vancouver, Canada, 7/10/18.

APA

Lu, F., & Tintarev, N. (2018). A Diversity Adjusting Strategy with Personality for Music Recommendation. In P. Brusilovsky, M. de Gemmis, A. Felfernig, P. Lops, J. O'Donovan, G. Semeraro, & M. C. Willemsen (Eds.), Proceedings of the 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (pp. 7-14). (CEUR Workshop Proceedings; Vol. 2225). CEUR.

Vancouver

Lu F, Tintarev N. A Diversity Adjusting Strategy with Personality for Music Recommendation. In Brusilovsky P, de Gemmis M, Felfernig A, Lops P, O'Donovan J, Semeraro G, Willemsen MC, editors, Proceedings of the 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems. CEUR. 2018. p. 7-14. (CEUR Workshop Proceedings).

Author

Lu, Feng ; Tintarev, Nava. / A Diversity Adjusting Strategy with Personality for Music Recommendation. Proceedings of the 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems. editor / Peter Brusilovsky ; Marco de Gemmis ; Alexander Felfernig ; Pasquale Lops ; John O'Donovan ; Giovanni Semeraro ; Martijn C. Willemsen. CEUR, 2018. pp. 7-14 (CEUR Workshop Proceedings).

BibTeX

@inproceedings{5661d5c79b3a41e885fce5d3050fc084,
title = "A Diversity Adjusting Strategy with Personality for Music Recommendation",
abstract = "Diversity-based recommender systems aim to select a wide rangeof relevant content for users, but diversity needs for users withdifferent personalities are rarely studied. Similarly, research onpersonality-based recommender systems has primarily focused onthe {\textquoteleft}cold-start problem{\textquoteright}; few previous works have investigated howpersonality influences users{\textquoteright} diversity needs. This paper combinesthese two branches of research together: re-ranking for diversifica-tion, and improving accuracy using personality traits. Anchoredin the music domain, we investigate how personality informationcan be used to adjust the diversity degrees for people with differentpersonalities. We proposed a personality-based diversification algo-rithm to help enhance the diversity adjusting strategy according topeople{\textquoteright}s personality information in music recommendations. Ouroffline and online evaluation results demonstrate that our proposedmethod is an effective solution to generate personalized recommen-dation lists that not only have relatively higher diversity as well asaccuracy, but which also lead to increased user satisfaction.",
keywords = "Recommender Systems, Diversity, Personality, Music Recommendation, Re-ranking",
author = "Feng Lu and Nava Tintarev",
year = "2018",
language = "English",
series = "CEUR Workshop Proceedings",
publisher = "CEUR",
pages = "7--14",
editor = "Peter Brusilovsky and {de Gemmis}, Marco and Alexander Felfernig and Pasquale Lops and John O'Donovan and Giovanni Semeraro and Willemsen, {Martijn C.}",
booktitle = "Proceedings of the 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems",
note = "IntRS 2018 : 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems ; Conference date: 07-10-2018 Through 07-10-2018",

}

RIS

TY - GEN

T1 - A Diversity Adjusting Strategy with Personality for Music Recommendation

AU - Lu, Feng

AU - Tintarev, Nava

PY - 2018

Y1 - 2018

N2 - Diversity-based recommender systems aim to select a wide rangeof relevant content for users, but diversity needs for users withdifferent personalities are rarely studied. Similarly, research onpersonality-based recommender systems has primarily focused onthe ‘cold-start problem’; few previous works have investigated howpersonality influences users’ diversity needs. This paper combinesthese two branches of research together: re-ranking for diversifica-tion, and improving accuracy using personality traits. Anchoredin the music domain, we investigate how personality informationcan be used to adjust the diversity degrees for people with differentpersonalities. We proposed a personality-based diversification algo-rithm to help enhance the diversity adjusting strategy according topeople’s personality information in music recommendations. Ouroffline and online evaluation results demonstrate that our proposedmethod is an effective solution to generate personalized recommen-dation lists that not only have relatively higher diversity as well asaccuracy, but which also lead to increased user satisfaction.

AB - Diversity-based recommender systems aim to select a wide rangeof relevant content for users, but diversity needs for users withdifferent personalities are rarely studied. Similarly, research onpersonality-based recommender systems has primarily focused onthe ‘cold-start problem’; few previous works have investigated howpersonality influences users’ diversity needs. This paper combinesthese two branches of research together: re-ranking for diversifica-tion, and improving accuracy using personality traits. Anchoredin the music domain, we investigate how personality informationcan be used to adjust the diversity degrees for people with differentpersonalities. We proposed a personality-based diversification algo-rithm to help enhance the diversity adjusting strategy according topeople’s personality information in music recommendations. Ouroffline and online evaluation results demonstrate that our proposedmethod is an effective solution to generate personalized recommen-dation lists that not only have relatively higher diversity as well asaccuracy, but which also lead to increased user satisfaction.

KW - Recommender Systems

KW - Diversity

KW - Personality

KW - Music Recommendation

KW - Re-ranking

M3 - Conference contribution

T3 - CEUR Workshop Proceedings

SP - 7

EP - 14

BT - Proceedings of the 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems

A2 - Brusilovsky, Peter

A2 - de Gemmis, Marco

A2 - Felfernig, Alexander

A2 - Lops, Pasquale

A2 - O'Donovan, John

A2 - Semeraro, Giovanni

A2 - Willemsen, Martijn C.

PB - CEUR

T2 - IntRS 2018

Y2 - 7 October 2018 through 7 October 2018

ER -

ID: 47509314