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

Abstract

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.

One-path Relaxed Realtime Constraint Mobile User Classification Method in Mobile Clickstreams

“…Church performed sessions and queries analysis of mobile Internet search with large real-world data 6) . The author conducted the regularity study on the mobile clickstreams and reported 80% accuracy in users that revisited the following month using statistical data on regularity 7) The author also did 80% true positive ratio for regularity in long-term mobile web access 8) . His research covered only day-scale behavior.…”

Intraday-scale Long Interval Method of Classifying Intramonth-Scale Revisiting Mobile Users

“…Church has performed sessions and queries analysis of mobile Internet searching with large real-world data [1]. The author conducted a regularity study on the mobile clickstream and reported 80 % accuracy in users that revisited the following month using statistical data on regularity [6] It showed an 80 % true positive ratio for regularity in long-term mobile web access [8]. However, this research covered only day-scale behavior.…”