Inferring search behaviors using partially observable markov model with duration (POMD)

• Authors: Yin He;Kuansan Wang
• Venue: Proceedings of the fourth ACM international conference on Web search and data mining
• Year: 2011

This paper presents Partially Observable Markov model with Duration (POMD), a statistical method that addresses the challenge of understanding sophisticated user behaviors from the search log in which some user actions, such as reading and skipping search results, cannot be observed and recorded. POMD utilizes not only the positional but also the temporal information of the clicks in the log. In this work, we treat the user engagements with a search engine as a Markov process, and model the unobservable engagements as hidden states. POMD differs from the traditional hidden Markov model (HMM) in that not all the hidden state transitions emit observable events, and that the duration of staying in each state is explicitly factored into the core statistical model. To address the training and decoding issues emerged as the results of the variations, we propose an iterative two-stage training algorithm and a greedy segmental decoding algorithm respectively. We validate the proposed algorithm with two sets of experiments. First, we show that the search behavioral patterns inferred by POMD match well with those reported in the eye tracking experiments. Secondly, through a series of A/B comparison experiments, we demonstrate that POMD can distinguish the ranking qualities of different search engine configurations much better than the patterns inferred by the model proposed in the previous work. Both of the experimental results suggest that POMD can provide a statistical and quantitative way to understand the sophisticated search behaviors by simply mining the search logs.