トピックモデルを用いたアクセスログからのユーザの状態推定

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2016/03/01

タイトル(英語): User Intent Estimation from Access Logs with Topic Model

著者名: 上辻　慶典（大阪府立大学），柳本　豪一（大阪府立大学），吉岡　理文（大阪府立大学）

著者名(英語): Keisuke Uetsuji (Osaka Prefecture University), Hidekazu Yanagimoto (Osaka Prefecture University), Michifumi Yoshioka (Osaka Prefecture University)

キーワード: アクセスログ，トピックモデル　　Access log，Topic model

要約(英語): As the Internet is widespread and there are many online shops in the Internet, many persons buy products in the online shops. Customer's behavior in the online shops is a sequence of customer driven activities intrinsically because his/her movement in an online shop occurs according to only his/her decision. Hence, to achieve satisfactory purchase experiments it is important how the shop supports them. Online shops have to predict customer's intents correctly to support them effectively. One of information resources the shops can use is an access log including information on customer's movement in the online shop. If they are histories of customer's behaviors in online shops and the behaviors depend on customer's intents, we can extract knowledge on them from the access logs. Speaking concretely, we can predict customers' intents from the access logs since their internal intents affect their activities. We can realized more appropriate recommendation service by changing recommendation strategy depending on customer's intents. In this paper, we propose a method to predict customer's intents from access logs in a real online shop. We adopt a Topic Tracking Model (TTM) to analyze the access logs.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.136 No.3 （2016） 特集：機械学習が拓くシステムイノベーション

本誌掲載ページ: 357-362 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/136/3/136_357/_article/-char/ja/