The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
Predicting users' requests on the WWW
UM '99 Proceedings of the seventh international conference on User modeling
Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
SALSA: the stochastic approach for link-structure analysis
ACM Transactions on Information Systems (TOIS)
Testing the Suitability of Markov Chains as Web Usage Models
COMPSAC '03 Proceedings of the 27th Annual International Conference on Computer Software and Applications
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions
IEEE Transactions on Knowledge and Data Engineering
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Proceedings of the 18th international conference on World wide web
Predicting query reformulation type from user behavior
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Discovering temporal hidden contexts in web sessions for user trail prediction
Proceedings of the 22nd international conference on World Wide Web companion
The self-feeding process: a unifying model for communication dynamics in the web
Proceedings of the 22nd international conference on World Wide Web
Session modeling to predict online buyer behavior
Proceedings of the 2013 workshop on Data-driven user behavioral modelling and mining from social media
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User modeling on the Web has rested on the fundamental assumption of Markovian behavior --- a user's next action depends only on her current state, and not the history leading up to the current state. This forms the underpinning of PageRank web ranking, as well as a number of techniques for targeting advertising to users. In this work we examine the validity of this assumption, using data from a number of Web settings. Our main result invokes statistical order estimation tests for Markov chains to establish that Web users are not, in fact, Markovian. We study the extent to which the Markovian assumption is invalid, and derive a number of avenues for further research.