Modeling User Activities on the Web using Paragraph Vector

Yukihiro Tagami, Hayato Kobayashi, Shingo Ono, Akira Tajima

The 24th International Conference on World Wide Web Companion (WWW2015) Posters, 2015/5


Natural Language Processing Machine Learning Data Science

Modeling user activities on the Web is a key problem for various Web services, such as news article recommendation and ad click prediction. In this paper, we propose an approach that summarizes each sequence of user activities using the Paragraph Vector, considering users and activities as paragraphs and words, respectively. The learned user representations are used among the user-related prediction tasks in common. We evaluate this approach on two data sets based on logs from Web services of Yahoo! JAPAN. Experimental results demonstrate the effectiveness of our proposed methods.

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