論 文Papers

CONFERENCE (INTERNATIONAL)

Cross-Domain Recommendation via Deep Domain Adaptation

Heishiro Kanagawa (UCL), Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami and Taiji Suzuki (UTokyo/RIKEN AIP)

41st European Conference on Information Retrieval (ECIR 2019), 2019/4

Category:

自然言語処理 (Natural Language Processing) 情報検索 (Information Retrieval) 機械学習 (Machine Learning)

Abstract:
The behavior of users in certain services indicates their preferences, which may be used to make recommendations for other services they have never used. However, the cross-domain relation between items and user preferences is not simple, especially when there are few or no common users and items across domains. We propose a content-based cross-domain recommendation method for cold-start users that does not require user- or item-overlap. We formulate recommendations as an extreme classification task, and the problem is treated as an instance of unsupervised domain adaptation. We assess the performance of the approach in experiments on large datasets collected from Yahoo! JAPAN video and news services and find that it outperforms several baseline methods including a cross-domain collaborative filtering method.
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Cross-Domain Recommendation via Deep Domain Adaptation(外部サイト/External Site Link)