Huaihua University, School of Music and Dance, Huaihua, Hunan Province 418000, China.
Comput Intell Neurosci. 2022 Jun 2;2022:2778181. doi: 10.1155/2022/2778181. eCollection 2022.
Music performance research is a comprehensive study of aspects such as emotional analysis and personalisation in music performance, which help to add richness and creativity to the art of music performance. The labels in this paper in collaborative annotation contain rich personalised descriptive information as well as item content information and can therefore be used to help provide better recommendations. The algorithm is based on bipartite graph node structure similarity and restarted random wandering. It analyses the connection between users, items, and tags in the music social network, firstly constructs the adjacency relationship between music and tags, obtains the music recommendation list and indirectly associated music collection, then fuses the results according to the proposed algorithm, and reorders them to obtain the final recommendation list, thus realising the personalised music recommendation algorithm. The experiments show that the proposed method can meet the personalised demand of users for music on this dataset.
音乐表演研究是对音乐表演中的情感分析和个性化等方面的综合研究，有助于为音乐表演艺术增添丰富性和创造性。本文协作标注中的标签包含丰富的个性化描述信息以及项目内容信息，因此可用于帮助提供更好的推荐。该算法基于二分图节点结构相似度和重启随机游走，分析音乐社交网络中用户、项目和标签之间的连接，首先构建音乐和标签之间的邻接关系，得到音乐推荐列表和间接关联的音乐收藏，然后根据提出的算法融合结果，并对其进行重新排序，以获得最终的推荐列表，从而实现个性化音乐推荐算法。实验表明，该方法可以满足该数据集上用户对音乐的个性化需求。
