A Stream-mining Oriented User Identification Algorithm Based on a Day Scale Click Regularity Assumption in Mobile Clickstreams

情報処理学会 Web Site

書誌事項

タイトル別名
  • データマイニング

この論文をさがす

抄録

The mobile Internet is characterized by “Easy-come and easy-go” characteristics which causes challenges for many content providers. The 24-hour clickstream provides a rich opportunity to understand user's behaviors. It also raises the challenge of having to cope with a large amount of log data. The author proposes a stream-mining oriented algorithm for user regularity classification. In the case study section the author shows the case studies in commercial mobile web sites and presents that the recall rate of the following month revisit prediction reaches 80?90%. The restriction of the stream mining gives a small gap to the recall rates in literature but the proposed method has the advantage of small working memory to perform the given task of identifying the high revisit ratio users.

The mobile Internet is characterized by “Easy-come and easy-go” characteristics, which causes challenges for many content providers. The 24-hour clickstream provides a rich opportunity to understand user's behaviors. It also raises the challenge of having to cope with a large amount of log data. The author proposes a stream-mining oriented algorithm for user regularity classification. In the case study section, the author shows the case studies in commercial mobile web sites and presents that the recall rate of the following month revisit prediction reaches 80窶骭€90%. The restriction of the stream mining gives a small gap to the recall rates in literature, but the proposed method has the advantage of small working memory to perform the given task of identifying the high revisit ratio users.

収録刊行物

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ