协同过滤算法及在个性化音乐推荐中的应用

Application of Collaborative Filtering Algorithm in Music Recommendation

当前互联网已进入了信息爆炸时期,但目前信息检索方法只能够从海量数据中检索出很小一部分比较热门的信息,而一些特定方面检索出的信息更少。基于协同过滤算法的个性化音乐推荐系统使用户能够从海量的音乐信息中很快寻找出自己感兴趣的音乐。协同过滤算法通过分析用户歌曲的播放、下载以及收藏等行为数据,计算用户之间的相似度,选取近邻用户,在近邻用户的喜好上预测目标用户的喜爱,克服了传统推荐方式的缺陷,实现了智能的个性化音乐推荐。

With the development of the Internet,too much information has brought the Internet into a period of information explosion. Relying on the current information retrieval method can only retrieve a small part of the more popular information from the massive data,and some specific aspects of the retrieved have less information. The personalized music recommendation system is optimized to enable users to find their favorite music from the massive amount of music information. In the process of realizing the personalized music recommendation system and algorithm,by analyzing the user’s behavior data such as song playback,download and collection,the similarity between users is calculated,the user is selected,the preference of the nearest neighbor user is predicted,and the defects of the traditional recommendation method are solved to a certain extent.