用音频特征预判新歌吸引力，提升音乐推荐多样性
"Beyond the past": Leveraging Audio and Human Memory for Sequential Music Recommendation
- 结合人类记忆模型与音频特征预测新歌热度
- 在真实数据上显著提升对未听歌曲的推荐效果
- 适合关注推荐系统多样性与跨域融合的研究者
在音乐流媒体服务中，听歌会话通常包含熟悉与新曲的平衡。近期，基于认知启发的顺序推荐系统（如ACT-R模型）已成功提升对下一会话相关歌曲的预测能力。然而，这类依赖用户历史行为的模型难以推荐用户从未听过的新曲。为此，本文提出一种新模型：利用音频信息预先预测新歌在类似ACT-R记忆机制下的激活值，并将其融入推荐评分。我们通过自有数据验证了该模型的有效性，并公开数据集与源代码，以促进该领域研究发展。
原文摘要 · Abstract (English)
On music streaming services, listening sessions are often composed of a balance of familiar and new tracks. Recently, sequential recommender systems have adopted cognitive-informed approaches, such as Adaptive Control of Thought-Rational (ACT-R), to successfully improve the prediction of the most relevant tracks for the next user session. However, one limitation of using a model inspired by human memory (or the past), is that it struggles to recommend new tracks that users have not previously listened to. To bridge this gap, here we propose a model that leverages audio information to predict in advance the ACT-R-like activation of new tracks and incorporates them into the recommendation scoring process. We demonstrate the empirical effectiveness of the proposed model using proprietary data, which we publicly release along with the model's source code to foster future research in this field.
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