An Improved Content-Based Music Recommending Method with Weighted Tags

Content-based filtering is widely used in music recommendation field. However, the performance of existing content-based methods is dissatisfactory, because those methods simply divided the listened songs into like or unlike set, and ignored user’s preference degree. In this paper, an enhanced content-based music recommending method was proposed by quantifying the user preference degree to songs with weighted tags. Firstly, each listened song was classified into like or unlike set according to user’s playing behaviors, such as skipping and repeating. Secondly, the songs’ social tags were collected from LastFm website and weighted according to their frequency in the collected tags.Finally, the user’s preference degree for each song was quantified with the weighted tags, and the candidate songs with high preference degrees would be recommended to him. On the LastFm dataset, the experimental results demonstrate that the proposed method outperforms those traditional content-based methods in both rating and ranking prediction.