Language intent models for inferring user browsing behavior

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Language intent models for inferring user browsing behavior
Modeling user browsing behavior is an active research area with tangible real-world applications, e.g., organizations can adapt their online presence to their visitors browsing behavior with positive effects in user engagement, and revenue. We concentrate on online news agents, and present a semisupervised method for predicting news articles that a user will visit after reading an initial article. Our method tackles the problem using language intent models trained on historical data which can cope with unseen articles. We evaluate our method on a large set of articles and in several experimental settings. Our results demonstrate the utility of language intent models for predicting user browsing behavior within online news sites. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Retrieval Models General Terms Algorithm, Experiment Keywords Online news, article intent models, user, browsing, behavior
Manos Tsagkias, Roi Blanco
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Authors Manos Tsagkias, Roi Blanco
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