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<!DOCTYPE html>
<html><head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8">
<meta charset="utf-8">
<meta name="viewport" content="width=device-width initial-scale=1">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<title>Analyzing Sequential User Behavior on the Web</title>
<meta name="description" content="The official resource for HypTrails.
">
<link rel="stylesheet" href="bootstrap.css">
<link rel="stylesheet" href="main.css">
<link rel="canonical" href="https://hyptrails.github.io/">
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<div class="container">
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<a class="navbar-brand" href="#">Tutorial</a>
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<ul class="nav navbar-nav">
<li><a href="#agenda">Agenda</a></li>
<li><a href="#slides">Slides</a></li>
<li><a href="#code">Source Code</a></li>
<li><a href="#references">References</a></li>
<li><a href="#presenters">Presenters</a></li>
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<div class="content">
<div class="container">
<a class="anchor" id="about"></a>
<div class="page-header">
<h1>Analyzing Sequential User Behavior on the Web</h1>
<img class="container" src="giraffe.jpg" style="padding:0px;padding-right:25px;">
</div>
<h2>Tutorial at the 25th International WWW conference</h2>
<p class=subheader> 12th of April 2016, Montreal <p>
<p>This tutorial aims at outlining fundamental methods for
studying categorical sequences on the Web. Categorical sequences
can refer to any kind of transitional data between
a set of states, for example human navigation (transitions)
between Web sites (states). Presented methods focus on sequential
pattern mining, modeling and inference aiming at
better understanding the production of sequences. A core
model utilized in this tutorial is the Markov chain model.
We hope that this tutorial raises interest and awareness of
the field at hand and provides participants with basic tools
for analyzing sequential user behavior on the Web.</p>
</div>
<div class="container">
<a class="anchor" id="agenda"></a>
<div class="page-header">
<h2>Agenda</h2>
</div>
<p class=subheader>The Tutorial will be structured into 4 parts:<p>
<ol>
<li> Introduction & Overview </li>
<li> Mining Sequential Patterns </li>
<li> Markov Chain Modeling </li>
<li> Comparing Hypotheses about Sequential Data (HypTrails) </li>
</ol>
</div>
<div class="container">
<a class="anchor" id="slides"></a>
<div class="page-header">
<h2>Slides</h2>
</div>
<p>Slides will be uploaded immediately after the Tutorial.<p>
</div>
<div class="container">
<a class="anchor" id="code"></a>
<div class="page-header">
<h2>Source Code and Notebooks</h2>
</div>
<p>In this tutorial, we provide Python code in the form of jupyter notebooks. These notebooks will be used throughout the tutorial but should also give attendees the opportunity to try things out and recap the material later on.</p>
<p class=subheader>The code can be found on <a href="https://github.com/sequenceanalysis/sequenceanalysis.github.io/tree/master/notebooks">github</a>.<br />
<p class=subheader>Running the notebooks</p>
<p> You can run/study the notebooks by the following three options:</p>
<ul>
<li class="run"><a href="http://mybinder.org/repo/sequenceanalysis/sequenceanalysis.github.io/notebooks">Interactive notebook environment on mybinder</a></li>
<li class="run"><a href="http://nbviewer.jupyter.org/github/sequenceanalysis/sequenceanalysis.github.io/tree/master/notebooks/">Rendered HTML notebooks on nbviewer</a></li>
<li class="run">Running <a href="https://github.com/sequenceanalysis/sequenceanalysis.github.io/tree/master/notebooks">code/notebooks</a> on your own: <a href="http://jupyter.org/">Jupyter notebook server</a>, <a href="https://www.continuum.io/downloads">Anaconda Python distribution</a></li>
</ul>
<p>Using the interactive notebook environment is probably the simplest way of running the notebooks. If you prefer to setup the notebooks on your notebook server, you should use Python 2.7 and the following list of packages need to be installed (e.g., by using pip): numpy, scipy and scikit-learn. We recommend to use the <a href="https://www.continuum.io/downloads">Anaconda Python distribution</a> that already includes the necessary packages.
<p>
<p class=subheader>Sequential Pattern Mining Part 2:</p>
<ul>
<li class="code">Jupyter Notebook Apriori</li>
<li class="code">Apriori Algorithm</li>
<li class="code">Jupyter Notebook PrefixSpan</li>
<li class="code">PrefixSpan Algorithm</li>
</ul>
<p class=subheader>Markov Chain Modeling Part 3:</p>
<ul>
<li class="code">Jupyter Notebook: <a href="https://github.com/sequenceanalysis/sequenceanalysis.github.io/blob/master/notebooks/part3.ipynb">[github]</a> <a href="http://mybinder.org/repo/sequenceanalysis/sequenceanalysis.github.io/notebooks/part3.ipynb">[mybinder]</a> <a href="http://nbviewer.jupyter.org/github/sequenceanalysis/sequenceanalysis.github.io/blob/master/notebooks/part3.ipynb">[nbviewer]</a></li>
</ul>
<p class=subheader>Comparing Hypotheses about Sequential Data (HypTrails) Part 4:</p>
<ul>
<li class="code">Jupyter Notebook: <a href="https://github.com/sequenceanalysis/sequenceanalysis.github.io/blob/master/notebooks/part4.ipynb">[github]</a> <a href="http://mybinder.org/repo/sequenceanalysis/sequenceanalysis.github.io/notebooks/part4.ipynb">[mybinder]</a> <a href="http://nbviewer.jupyter.org/github/sequenceanalysis/sequenceanalysis.github.io/blob/master/notebooks/part4.ipynb">[nbviewer]</a></li>
<li class="code">Additional notebook: <a href="http://nbviewer.jupyter.org/github/psinger/HypTrails/blob/master/tutorial/hyptrails_tutorial.ipynb">[nbviewer]</a></li>
</ul>
</div>
<div class="container">
<a class="anchor" id="references"></a>
<div class="page-header">
<h2>References</h2>
</div>
<p class=subheader>Key References:<p>
<ul>
<li class="code">Mooney, C. H., & Roddick, J. F. (2013). Sequential pattern mining--approaches and algorithms. ACM Computing Surveys (CSUR), 45(2), 19.</li>
<li class="code">Singer, P., Helic, D., Taraghi, B., & Strohmaier, M. (2014). Detecting memory and structure in human navigation patterns using markov chain models of varying order. PloS one, 9(7), e102070.</li>
<li class="code">Singer, P., Helic, D., Hotho, A., & Strohmaier, M. (2015, May). Hyptrails: A bayesian approach for comparing hypotheses about human trails on the web. In Proceedings of the 24th International Conference on World Wide Web (pp. 1003-1013). International World Wide Web Conferences Steering Committee.</li>
</ul>
<p class=subheader>Further References References:<p>
<ul>
<li class="code">tbd...</li>
</ul>
</div>
<div class="container">
<a class="anchor" id="presenters"></a>
<div class="page-header">
<h2>Presenters</h2>
</div>
<!--p class="lead">Work around HypTrails is a collaboration of several organizations.</p-->
<div class="presenters">
<ul>
<li><div>
<img class="logo" src="singer.jpg" />
<div class="desc">Philipp Singer<br />
<a href="mailto:philipp.singer@gesis.org">philipp.singer@gesis.org</a>
<br /><a href="http://www.philippsinger.info/">http://www.philippsinger.info/</a>
<br/><a href="https://twitter.com/ph_singer">@ph_singer</a>
</div>
</div></li>
<li><div>
<img class="logo" src="lemmerich.png" />
<div class="desc">Florian Lemmerich<br><a href="mailto:florian.lemmerich@gesis.org">florian.lemmerich@gesis.org</a><br><a href="http://florian.lemmerich.net/">http://florian.lemmerich.net/</a></div>
</div></li>
</ul>
<p><font size="1em">CC image courtesy of user puliarfanita on
<a href="https://www.flickr.com/photos/puliarfanita/6648892997/">Flickr</a></font></p>
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