JSAI2018
Jun 5 - Jun 8, 2018Kagoshima-shi, Kagoshima-ken (Shiroyama Kanko Hotel)
JSAI2018
Jun 5 - Jun 8, 2018Kagoshima-shi, Kagoshima-ken (Shiroyama Kanko Hotel)
[1C1-04]Combining semi-supervised learning and singular value decomposition to Cold-Start problem
〇Takumi Uchida1, Kei Nakagawa1,2, Kenichi Yoshida1(1. University of Tukuba, 2. Nomura Asset Management Co., Ltd)
Keywords:
Cold-Start problem,semi-supervised learning,recommender system
The Cold-Start is one of the problems in web marketing. For example, when the system recommend items to users on an e-commerce site, a purchase log and a review log are handled. However, since web marketing data tends to be long tail, log data of most users and items are few to learn. In recommender system, the item is presented to the user based on this past log, thus long tail items are hardly presented than popular items. In this research, we propose the method combining semi-supervised learning and singular value decomposition against this Cold-Start problem. In addtion, we report the result of verifying our proposed method with the user rating score of the movie provided by MovieLens.