Exploring longitudinal effects of session-based recommendations

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• dc.contributor.author Ferraro, Andrés
• dc.contributor.author Jannach, Dietmar
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2021-02-09T16:40:25Z
• dc.date.available 2021-02-09T16:40:25Z
• dc.date.issued 2020
• dc.description Comunicació presentada a: 14th ACM Conference on Recommender Systems celebrat del 22 al 26 de setembre de 2020 de manera virtual.
• dc.description.abstract Session-based recommendation is a problem setting where the task of a recommender system is to make suitable item suggestions based only on a few observed user interactions in an ongoing session. The lack of long-term preference information about individual users in such settings usually results in a limited level of personalization, where a small set of popular items may be recommended to many users. This repeated exposure of such a subset of the items through the recommendations may in turn lead to a reinforcement effect over time, and to a system which is not able to help users discover new content anymore to the desirable extent. In this work, we investigate such potential longitudinal effects of session-based recommendations in a simulation-based approach. Specifically, we analyze to what extent algorithms of different types may lead to concentration effects over time. Our experiments in the music domain reveal that all investigated algorithms—both neural and heuristic ones—may lead to lower item coverage and to a higher concentration on a subset of the items. Additional simulation experiments however also indicate that relatively simple re-ranking strategies, e.g., by avoiding too many repeated recommendations in the music domain, may help to deal with this problem.
• dc.format.mimetype application/pdf
• dc.identifier.citation Ferraro A, Jannach D, Serra X. Exploring longitudinal effects of session-based recommendations. In: RecSys '20: Fourteenth ACM Conference on Recommender Systems; 2020 Sep 22–26; Brazil. New York: Association for Computing Machinery; 2020. p. 474-9. DOI: 10.1145/3383313.3412213
• dc.identifier.doi http://dx.doi.org/10.1145/3383313.3412213
• dc.identifier.isbn 978-1-4503-7583-2
• dc.identifier.uri http://hdl.handle.net/10230/46403
• dc.language.iso eng
• dc.publisher ACM Association for Computer Machinery
• dc.relation.ispartof RecSys '20: Fourteenth ACM Conference on Recommender Systems; 2020 Sep 22–26; Brazil. New York: Association for Computing Machinery; 2020.
• dc.rights © 2020 Association for Computing Machinery
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Session-based Recommendation
• dc.subject.keyword Longitudinal Effects
• dc.subject.keyword Bias
• dc.title Exploring longitudinal effects of session-based recommendations
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion