Department of Music, Handan University, Handan, Hebei 056005, China.
Comput Intell Neurosci. 2022 Feb 17;2022:7157075. doi: 10.1155/2022/7157075. eCollection 2022.
Aiming at the shortcomings of traditional recommendation algorithms in dealing with large-scale music data, such as low accuracy and poor real-time performance, a personalized recommendation algorithm based on the Spark platform is proposed. The algorithm is based on the Spark platform. The K-means clustering model between users and music is constructed using an AFSA (artificial fish swarm algorithm) to optimize the initial centroids of K-means to improve the clustering effect. Based on the scoring relationship between users and users and users and music attributes, the collaborative filtering algorithm is applied to calculate the correlation between users to achieve accurate recommendations. Finally, the performance of the designed recommendation model is validated by deploying the recommendation model on the Spark platform using the Yahoo Music dataset and online music platform dataset. The experimental results show that the use of improved AFSA can complete the optimization of K-means clustering centroids with good clustering results; combined with the distributed fast computing capability of Spark platform with multiple nodes, the recommendation accuracy has better performance than traditional recommendation algorithms; especially when dealing with large-scale music data, the recommendation accuracy and real-time performance are higher, which meet the current demand of personalized music recommendation.
针对传统推荐算法在处理大规模音乐数据时存在的准确性低、实时性差等缺点，提出了一种基于 Spark 平台的个性化推荐算法。该算法基于 Spark 平台，利用人工鱼群算法（AFSA）构建用户与音乐之间的 K-means 聚类模型，优化 K-means 的初始质心，提高聚类效果。基于用户与用户、用户与音乐属性之间的评分关系，应用协同过滤算法计算用户之间的相关性，实现精准推荐。最后，使用雅虎音乐数据集和在线音乐平台数据集在 Spark 平台上部署推荐模型验证设计推荐模型的性能。实验结果表明，改进的 AFSA 能够完成 K-means 聚类质心的优化，具有较好的聚类效果；结合 Spark 平台多节点的分布式快速计算能力，推荐准确率具有更好的性能，优于传统推荐算法；特别是在处理大规模音乐数据时，推荐准确率和实时性更高，满足了当前个性化音乐推荐的需求。
