Music Recommendation based on Dynamic User Interests

• Main Authors: Kun-Wei Han, 韓鯤偉
• Other Authors: Pu-Jen Cheng
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/38260766526464662046

碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 102 === In this paper, we propose a dynamic weight tuning scheme for online mu- sic recommendation. Based on a latent factor model, songs, artists, and users are mapped into a latent space. Then, given each user’s recent songs we can determine his current interest for music, which either similar to his past be- havior or more like recent ones. Like latent factor based models, this scheme can be trained without content information, which is a benefit when adopting internet radios as data source. Experimental results on the last.fm collections show that our proposed method is effective. Keywords: Music Recommendation, Dynamic Interests, Latent Factor Model, Machine Learning, Gradient Ascent