A Bayesian approach to predicting website revisitation on mobile phones

Jeffrey C. Zemla, Chad C. Tossell, Philip Kortum, Michael D. Byrne

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Mobile web browsing is highly recurrent, in that a large proportion of user's page requests are to a small set of websites. Despite this, most mobile browsers do not provide an efficient means for revisiting sites. Although significant research exists on prediction in the personal computer realm, little work has been done in the mobile realm where physical constraints of the device and mobile browsing behaviors are vastly different. The current research proposes a Bayesian model approach, based on a cognitive model of memory retrieval that integrates multiple cues in order to predict the next site a user will visit. These cues include frequency of site visitation, the recency of site visitation, and the context in which specific sites are accessed. The model is assessed using previously collected web logs from 24 iPhone users over the course of one year. Our model outperforms simpler models based on frequency or recency, which are sometimes implemented in desktop browsers. Potential applications of the model are discussed with the objective of increasing browsing efficiency on mobile devices.

Original languageEnglish (US)
Pages (from-to)43-50
Number of pages8
JournalInternational Journal of Human Computer Studies
StatePublished - Jul 10 2015
Externally publishedYes


  • Computational modeling
  • Mobile phones
  • World wide web

ASJC Scopus subject areas

  • Software
  • General Engineering
  • Education
  • Human Factors and Ergonomics
  • Human-Computer Interaction
  • Hardware and Architecture


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