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...

• Type: Conference
• Year: 2010
• Where: WSDM
• Authors: Kuansan Wang, Nikolas Gloy, Xiaolong Li
• Keywords: Data Mining, Observable Markov Model, POM Model Parameters, Sophisticated Search Behaviors, WSDM 2010