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Representation Learning for Users' Web Browsing Sequences
Yukihiro TAGAMI Hayato KOBAYASHI Shingo ONO Akira TAJIMA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2018/07/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
online advertising, Web browsing behavior, Paragraph Vector, representation learning,
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Modeling user activities on the Web is a key problem for various Web services, such as news article recommendation and ad click prediction. In our work-in-progress paper, we introduced an approach that summarizes each sequence of user Web page visits using Paragraph Vector, considering users and URLs as paragraphs and words, respectively. The learned user representations are used among the user-related prediction tasks in common. In this paper, on the basis of analysis of our Web page visit data, we propose Backward PV-DM, which is a modified version of Paragraph Vector. We show experimental results on two ad-related data sets based on logs from Web services of Yahoo! JAPAN. Our proposed method achieved better results than those of existing vector models.