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2010
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Inferring Search Behaviors Using Partially Observable Markov (POM) Model

9 years 8 months ago
Inferring Search Behaviors Using Partially Observable Markov (POM) Model
This article describes an application of the partially observable Markov (POM) model to the analysis of a large scale commercial web search log. Mathematically, POM is a variant of the hidden Markov model in which all the hidden state transitions do not necessarily emit observable events. This property of POM is used to model, as the hidden process, a common search behavior that users would read and skip search results, leaving no observable user actions to record in the search logs. The Markov nature of the model further lends support to cope with the facts that a single observed sequence can be probabilistically associated with many hidden sequences that have variable lengths, and the search results can be read in various temporal orders that are not necessarily reflected in the observed sequence of user actions. To tackle the implementation challenges accompanying the flexibility and analytic powers of POM, we introduce segmental Viterbi algorithm based on segmental decoding and Vi...
Kuansan Wang, Nikolas Gloy, Xiaolong Li
Added 01 Mar 2010
Updated 02 Mar 2010
Type Conference
Year 2010
Where WSDM
Authors Kuansan Wang, Nikolas Gloy, Xiaolong Li
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