@article{
 title = {Navigation pattern discovery using grammatical inference},
 type = {article},
 year = {2004},
 pages = {187–198},
 websites = {http://www.springerlink.com/index/N8CBH619BV6415BH.pdf},
 publisher = {Springer},
 id = {4af25341-59be-3841-b2b2-656c18c8eef3},
 created = {2012-04-01T16:32:49.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karampatziakis2004},
 private_publication = {false},
 abstract = {We present a method for modeling user navigation on a web site using grammatical inference of stochastic regular grammars. With this method we achieve better models than the previously used first order Markov chains, in terms of predictive accuracy and utility of recommendations. In order to obtain comparable results, we apply the same grammatical inference algorithms on Markov chains, modeled as probabilistic automata. The automata induced in this way perform better than the original Markov chains, as models for user navigation, but they are considerably inferior to the automata induced by the traditional grammatical inference methods. The evaluation of our method was based on two web usage data sets from two very dissimilar web sites. It consisted in producing, for each user, a personalized list of recommendations and then measuring its recall and expected utility.},
 bibtype = {article},
 author = {Karampatziakis, N and Paliouras, G and Pierrakos, D and Stamatopoulos, P},
 journal = {Lecture Notes in Computer Science}
}

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