Documents

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.
Original languageEnglish
Title of host publicationProceedings of the 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems
EditorsPeter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, John O'Donovan, Giovanni Semeraro, Martijn C. Willemsen
PublisherCEUR
Pages7-14
Number of pages8
Publication statusPublished - 2018
EventIntRS 2018: 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems - Vancouver, Canada
Duration: 7 Oct 20187 Oct 2018

Publication series

NameCEUR Workshop Proceedings
Volume2225
ISSN (Print)1613-0073

Workshop

WorkshopIntRS 2018
CountryCanada
CityVancouver
Period7/10/187/10/18

    Research areas

  • Recommender Systems, Diversity, Personality, Music Recommendation, Re-ranking

ID: 47509314