A Web page prediction model based on click-stream tree representation of user behavior

Sule Gündüz*, M. Tamer Zsu

*Bu çalışma için yazışmadan sorumlu yazar

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136 Atıf (Scopus)

Özet

Predicting the next request of a user as she visits Web pages has gained importance as Web-based activity increases. Markov models and their variations, or models based on sequence mining have been found well suited for this problem. However, higher order Markov models are extremely complicated due to their large number of states whereas lower order Markov models do not capture the entire behavior of a user in a session. The models that are based on sequential pattern mining only consider the frequent sequences in the data set, making it difficult to predict the next request following a page that is not in the sequential pattern. Furthermore, it is hard to find models for mining two different kinds of information of a user session. We propose a new model that considers both the order information of pages in a session and the time spent on them. We cluster user sessions based on their pair-wise similarity and represent the resulting clusters by a click-stream tree. The new user session is then assigned to a cluster based on a similarity measure. The click-stream tree of that cluster is used to generate the recommendation set. The model can be used as part of a cache prefetching system as well as a recommendation model.

Orijinal dilİngilizce
Sayfalar535-540
Sayfa sayısı6
DOI'lar
Yayın durumuYayınlandı - 2003
Etkinlik9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03 - Washington, DC, United States
Süre: 24 Ağu 200327 Ağu 2003

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???event.eventtypes.event.conference???9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03
Ülke/BölgeUnited States
ŞehirWashington, DC
Periyot24/08/0327/08/03

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