Abstract
Recently, researchers have started to model interactions between users and search engines as an online learning ranking. Such systems obtain feedback only on the few top-ranked documents results. To obtain feedbacks on other documents, the system has to explore the non-top-ranked documents that could lead to a better solution. However, the system also needs to ensure that the quality of result lists is high by exploiting what is already known. Clearly, this results in an exploration/exploitation dilemma. We introduce in this paper an algorithm that tackles this dilemma in Context-Based Information Retrieval (CBIR) area. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user’s situation is most relevant for exploration or exploitation. Within a deliberately designed online framework we conduct evaluations with mobile users. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.
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Bouneffouf, D., Bouzeghoub, A., Gançarski, A.L. (2013). Contextual Bandits for Context-Based Information Retrieval. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_5
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DOI: https://doi.org/10.1007/978-3-642-42042-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42041-2
Online ISBN: 978-3-642-42042-9
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