Publications
OTHERS (INTERNATIONAL) Cross-domain Recommendation via Deep Domain Adaptation
Heishiro Kanagawa(Tokyo Tech), Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami, Taiji Suzuki
arXiv.org (arXiv.org)
March 09, 2018
The behavior of users in certain services could be a clue that can be used to infer their preferences and may be used to make recommendations for other services they have never used. However, the cross-domain relationships between items and user consumption patterns are not simple, especially when there are few or no common users and items across domains. To address this problem, we propose a content-based cross-domain recommendation method for cold-start users that does not require user- and item- overlap. We formulate recommendation as extreme multi-class classification where labels (items) corresponding to the users are predicted. With this formulation, the problem is reduced to a domain adaptation setting, in which a classifier trained in the source domain is adapted to the target domain. For this, we construct a neural network that combines an architecture for domain adaptation, Domain Separation Network, with a denoising autoencoder for item representation. We assess the performance of our approach in experiments on a pair of data sets collected from movie and news services of Yahoo! JAPAN and show that our approach outperforms several baseline methods including a cross-domain collaborative filtering method.
Paper :
Cross-domain Recommendation via Deep Domain Adaptation
(external link)