- Language Intent Models for Inferring User Browsing Behavior
- Book/source title
- SIGIR'12: the proceedings of the International ACM SIGIR Conference on Research & Development in Information Retrieval: August 12-16, 2012: Portland, Oregon, USA
- Pages (from-to)
- New York, NY: Association for Computing Machinery
- Document type
- Conference contribution
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
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 semi-supervised 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.
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