An Analysis of Search Engine Switching Behavior Using Click Streams

In this paper, we propose a simple framework to characterize the switching behavior between search engines based on click streams. We segment users into a number of categories based on their search engine usage during two adjacent time periods and construct the transition probability matrix across these usage categories. The principal eigenvector of the transposed transition probability matrix represents the limiting probabilities, which are proportions of users in each usage category at steady state. We experiment with this framework using click streams focusing on two search engines: one with a large market share and the other with a small market share. The results offer interesting insights into search engine switching. The limiting probabilities provide empirical evidence that small engines can still retain its fair share of users over time. Categories and Subject Descriptors Search Engines, Data Mining General Terms Sequence, Session, Markov Chain, Principal Eigenvectors Keywords...

• Type: Conference
• Year: 2005
• Where: WINE
• Authors: Yun-Fang Juan, Chi-Chao Chang
• Keywords: Economy, Search Engines, Switching Behavior, Transition Probability Matrix, WINE 2005