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Communication Dans Un Congrès Année : 2016

Web usage prediction and recommendation using web session clustering

Résumé

In recent years, a strong interest has been given to web usage prediction and recommendation methods to improve e-commerce, search engines and other online applications. There have been various efforts carried out in this field, particularly focused on using recordings of web user interactions with websites. In this context, our research focuses on developing a novel approach for web prediction and recommendation. The proposed method relies on hierarchical session clustering by sequence similarity measure and takes advantage of access activity time and access position in prediction session to make a recommendation. The performed experiments reveal that hierarchical parameter and prediction accuracy are relevant. In addition, the paper introduces cost estimation to adapt web visitor behavior to web business purposes using prediction and recommendation results.
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Dates et versions

hal-03807679 , version 1 (10-10-2022)

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Citer

Vinh-Trung Luu, Germain Forestier, Mathis Ripken, Frederic Fondement, Pierre-Alain Muller. Web usage prediction and recommendation using web session clustering. International Conference on Digital Information Management (ICDIM), Sep 2016, Porto, France. pp.107-113, ⟨10.1109/icdim.2016.7829779⟩. ⟨hal-03807679⟩

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