摘要
针对用户情境信息,提出一种融合分类与协同过滤的情境感知音乐推荐算法.首先,通过计算用户情境信息的相似度,由协同过滤算法得到初始音乐推荐列表;然后通过机器学习算法训练分类模型,得出用户在特定情境下的音乐类型偏好;最后将协同过滤得到的推荐列表与分类模型得到的音乐类型偏好进行融合,为特定情境的用户提供个性化音乐推荐.该算法不仅有效地降低了推荐过程的复杂度,还使传统的协同过滤推荐算法具备了情境感知的能力.实验结果表明,该方法可以有效地提高个性化音乐推荐系统的性能.
A context-aware music recommendation algorithm combining classification and collaborative filtering was proposed to solve the problem of how to use context information for personalized recommendation.First,this paper was adopted a method of calculating the similarity of user context information,and was used the collaborative filtering algorithm to obtain the initial music recommendation list.Then the classification model had been trained by the machine learning algorithm to derive the user’s music type preference in a specific context.The recommended list generated by collaborative filtering algorithm was merged with the music type preference predicted by the classifier to provide personalized music recommendations for users in specific situations.The algorithm not only effectively reduced the complexity of the recommendation process,but also enabled the traditional collaborative filtering recommendation algorithm to have context-awareness.Experimental results had proved that this method can effectively improve the performance of personalized music recommendation system.
作者
吴海金
陈俊
WU Haijin;CHEN Jun(College of Physics and Information Engineering,Fuzhou University,Fuzhou,Fujian 350108,China)
出处
《福州大学学报（自然科学版）》
CAS
北大核心
2019年第4期467-471,共5页
Journal of Fuzhou University(Natural Science Edition)
基金
国家自然科学基金资助项目(61571129)
关键词
音乐推荐
个性化推荐
情境感知
协同过滤
music recommendation
personalized recommendation
context-aware
collaborative filtering
