Predicting Users’ Revisitation Behaviour Based on Web Access Contextual Clusters

Toba, Hapnes and Jomei, Christopher Starry and Setiawan, Lotanto and Karnalim, Oscar and Li, Hui (2020) Predicting Users’ Revisitation Behaviour Based on Web Access Contextual Clusters. In: The 8th International Conference on Information Communication and Technology (ICoICT), 24-26 Juni 2020, Yogyakarta, Indonesia.

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Abstract

Most modern browsers record all previously visited web pages for future revisitation. However, not all users utilise such feature. One of the reasons is that the records are displayed at once as a single list, which may overwhelm the users. This paper proposes a predictive model to decide whether a web page will be revisited in the future based on a particular visit. The model can be used to filter web records so that only web pages that may be re-visited are presented. According to our evaluation, the model is considerably effective. It can generate 53.195% accuracy when measured with 10-fold cross validation and 95% meaningful topic identification. Further, attributes rooted from the same website’ access frequency are the most salient ones for prediction. In addition, contextual similarities based on k-means clustering and cosine similarity (which are used for defining some attributes) are considerably effective.

Item Type: Conference or Workshop Item (Paper)
Contributors:
ContributionContributorsNIDN/NIDKEmail
UNSPECIFIEDToba, HapnesUNSPECIFIEDUNSPECIFIED
UNSPECIFIEDJomei, Christopher StarryUNSPECIFIEDUNSPECIFIED
UNSPECIFIEDSetiawan, LotantoUNSPECIFIEDUNSPECIFIED
UNSPECIFIEDKarnalim, OscarUNSPECIFIEDUNSPECIFIED
UNSPECIFIEDLi, HuiUNSPECIFIEDUNSPECIFIED
Uncontrolled Keywords: browsing history, cosine similarity, k-means clustering, multinomial naïve bayes, user log
Subjects: T Technology > T Technology (General)
Depositing User: Perpustakaan Maranatha
Date Deposited: 05 Oct 2023 08:13
Last Modified: 05 Oct 2023 08:13
URI: http://repository.maranatha.edu/id/eprint/32159

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