广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (04): 37-44.doi: 10.12052/gdutxb.220009
吴亚迪, 陈平华
Wu Ya-di, Chen Ping-hua
摘要: 针对现有音乐推荐在用户偏好建模时忽略用户长期偏好,或对用户记录统一建模时忽略历史信息与当前情境联系的问题,提出一种基于用户长短期偏好和音乐情感注意力的音乐推荐模型。首先将用户听歌记录切分为多个历史序列和当前序列,利用多个长短期记忆网络分别进行特征提取,得到用户长短期偏好:对于历史音乐序列,提出序列时段的概念,并进行序列时段加权计算,得到长期偏好;对于当前序列,利用平均池化提取当前情景音乐特征,得到短期偏好。其次,从音乐声学信号中学习音乐情感特征,应用注意力机制计算音乐情感因子。最后,将音乐情感因子融入用户长短期偏好,得到一个音乐推荐列表。在Last.fm真实数据集上的实验结果表明,模型的NDCG@10达到了0.5435,优于现有方法;消融实验和特征贡献分析进一步验证了模型的有效性。
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