使用 ACT-R 框架预测音乐重听行为
Predicting Music Relistening Behavior Using the ACT-R Framework
AI总结：
本文基于ACT-R认知理论的五个组件建模并预测音乐重听行为，发现曲目接触新近度与频率是有效预测因子，且考虑曲目共现和熟悉度可进一步提升R-precision性能。
摘要：
提供合适的推荐对于提高音乐推荐系统的用户满意度至关重要。在这里，用户经常重复收听同一曲目并多次欣赏同一首歌曲的推荐。因此，考虑用户的重听行为对于音乐推荐系统至关重要。在本文中，我们描述了一种基于心理学的方法来建模和预测音乐重听行为，该方法受音乐心理学研究的启发，这些研究将音乐偏好与人类记忆联系起来。我们采用了一种成熟的人类认知心理学理论，该理论对人类记忆的操作进行建模，即 Adaptive Control of Thought-Rational (ACT-R)。与仅使用 ACT-R 基础水平组件的先前工作不同，我们利用了 ACT-R 的五个组件，即基础水平、扩散、部分匹配、估值和噪声，来研究五个因素对音乐重听行为的影响：先前接触曲目的新近度和频率，曲目的共现，曲目之间的相似性，对曲目的熟悉度，以及行为中的随机性。在来自 Last.fm 的 170 万次收听事件的数据集上，我们通过顺序预测用户会话中的下一个曲目来评估我们方法的性能。我们发现，先前接触曲目的新近度和频率是重听行为的有效预测因子。此外，考虑曲目的共现和对曲目的熟悉度进一步提高了 R-precision 方面的性能。我们希望我们的工作能激发未来关于考虑记忆检索的认知方面以建模和预测复杂用户行为的价值的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
