音乐推荐系统中的用户冷启动问题

利用新用户使用音乐时的上下文环境信息来解决冷启动问题.当用户播放音乐时，提取用户当时场景的时间、天气、状态等信息，利用这些信息构建决策树分类模型.当新用户使用音乐推荐系统时，提取当前情景的上下文信息，根据决策树分支和上下文信息判断新用户的兴趣，向新用户推荐音乐.实验结果表明，当采用上下文相关音乐推荐算法，推荐数目不超过6项时，新用户对推荐结果的满意度超过TopN算法.

Problem of User Cold-Start in Music Recommendation System

This paper addresses the cold-start problem based on the context of new users'playing music.The decision tree classification model is constructed with the context information of time，weather，state when users play music.When new users use the music recommendation system，their context information is extracted and their interests are estimated according to the branch of the decision tree and the context information so that certain music is recommended to them.The experimental results show that when the recommended music does not exceed six items，new users are more satisfied with the recommendations than with the recommendations offered by TopN.