Predicting music relistening behavior using the ACT-R framework

Markus Reiter-Haas, Emilia Parada-Cabaleiro, Markus Schedl, Elham Motamedi, Marko Tkalcic, Elisabeth Lex

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

Providing suitable recommendations is of vital importance to improve the user satisfaction of music recommender systems. Here, users often listen to the same track repeatedly and appreciate recommendations of the same song multiple times. Thus, accounting for users' relistening behavior is critical for music recommender systems. In this paper, we describe a psychology-informed approach to model and predict music relistening behavior that is inspired by studies in music psychology, which relate music preferences to human memory. We adopt a well-established psychological theory of human cognition that models the operations of human memory, i.e., Adaptive Control of Thought-Rational (ACT-R). In contrast to prior work, which uses only the base-level component of ACT-R, we utilize five components of ACT-R, i.e., base-level, spreading, partial matching, valuation, and noise, to investigate the effect of five factors on music relistening behavior: (i) recency and frequency of prior exposure to tracks, (ii) co-occurrence of tracks, (iii) the similarity between tracks, (iv) familiarity with tracks, and (v) randomness in behavior. On a dataset of 1.7 million listening events from Last.fm, we evaluate the performance of our approach by sequentially predicting the next track(s) in user sessions. We find that recency and frequency of prior exposure to tracks is an effective predictor of relistening behavior. Besides, considering the co-occurrence of tracks and familiarity with tracks further improves performance in terms of R-precision. We hope that our work inspires future research on the merits of considering cognitive aspects of memory retrieval to model and predict complex user behavior.

Originalspracheenglisch
TitelRecSys 2021 - 15th ACM Conference on Recommender Systems
Herausgeber (Verlag)Association of Computing Machinery
Seiten702-707
Seitenumfang6
ISBN (elektronisch)9781450384582
DOIs
PublikationsstatusVeröffentlicht - 13 Sep 2021
Veranstaltung15th ACM Conference on Recommender Systems: RECSYS 2021 - Amsterdam, Niederlande
Dauer: 27 Sep 20211 Okt 2021

Konferenz

Konferenz15th ACM Conference on Recommender Systems
KurztitelRECSYS 2021
LandNiederlande
OrtAmsterdam
Zeitraum27/09/211/10/21

ASJC Scopus subject areas

  • !!Control and Systems Engineering
  • !!Computer Networks and Communications
  • !!Hardware and Architecture

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