Personal artist recommendation via a listening and trust preference network

Trust information provided by a user unfolds his/her reliable friends with similar tastes. It not only has the potential to help provide better recommendations but also emancipates the recommendation process from heavy computation for seeking friends. In this paper, by taking into account the latent value of trust information, our personal artist recommendation algorithm via a listening and trust preference network (LTPN for short) is presented. We argue that the excellent recommendation should be acquired via the listening and trust preference network instead of the original listening and trust relation information. Experimental results demonstrate LTPN can not only provide better recommendation but also help relieve the cold start problem caused by new users.