How playlist evaluation compares to track evaluations in music recommender systems
| Autor: | Hadash, Sophia, Liang, Yu, Willemsen, Martijn C., Brusilovsky, P., de Gemmis, M., Felfernig, A., Lops, P., O'Donovan, J., Semeraro, G., Willemsen, M.C. |
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| Přispěvatelé: | Human Technology Interaction |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | IntRS 2019 Interfaces and Human Decision Making for Recommender Systems 2019: Proceedings of the 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with 13th ACM Conference on Recommender Systems (RecSys 2019) Copenhagen, Denmark, September 19, 2019, 1-9 STARTPAGE=1;ENDPAGE=9;TITLE=IntRS 2019 Interfaces and Human Decision Making for Recommender Systems 2019 |
| Popis: | Most recommendation evaluations in music domain are focused on algorithmic performance: how a recommendation algorithm could predict a user's liking of an individual track. However, individual track rating might not fully reflect the user's liking of the whole recommendation list. Previous work has shown that subjective measures such as perceived diversity and familiarity of the recommendations, as well as the peak-end effect can influence the user's overall (holistic) evaluation of the list. In this study, we investigate how individual track evaluation compares to holistic playlist evaluation in music recommender systems, especially how playlist attractiveness is related to individual track rating and other subjective measures (perceived diversity) or objective measures (objective familiarity, peak-end effect and occurrence of good recommendations in the list). We explore this relation using a within-subjects online user experiment, in which recommendations for each condition are generated by different algorithms. We found that individual track ratings can not fully predict playlist evaluations, as other factors such as perceived diversity and recommendation approaches can influence playlist attractiveness to a larger extent. In addition, inclusion of the highest and last track rating (peak-end) is equally good in predicting playlist attractiveness as the inclusion of all track evaluations. Our results imply that it is important to consider which evaluation metric to use when evaluating recommendation approaches. |
| Databáze: | OpenAIRE |
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