MixedTrails: Bayesian Hypothesis Comparison
                         Heterogeneous Sequential Data

                                Martin Becker1 , Philipp Singer2 Florian Lemmerich2 ,
                                   Andreas Hotho1,3 , and Markus Strohmaier2,4
                                            1
                                         University of Würzburg, Germany
                                  {becker,hotho}@informatik.uni-wuerzburg.de
                                           2
                                              GESIS, Cologne, Germany
                        {philipp.singer,florian.lemmerich,markus.strohmaier}@gesis.org
                                    3
                                      L3S Research Center, Hannover, Germany
                                 {philipp.singer,markus.strohmaier}@gesis.org
                                 4
                                   University of Koblenz-Landau, Mainz, Germany

                     Sequential traces of user data are frequently observed online and offline, e.g.,
                 as sequences of visited websites or as sequences of locations captured by GPS.
                 However, understanding factors explaining the production of sequence data is a
                 challenging task, especially since the data generation is often not homogeneous.
                 For example, navigation behavior might change in different phases of browsing
                 a website, or movement behavior may vary between groups of users. In this
                 work, we tackle this task and propose MixedTrails [1], a Bayesian approach for
                 comparing the plausibility of hypotheses regarding the generative processes of
                 heterogeneous sequence data. Each hypothesis is derived from existing literature,
                 theory or intuition and represents a belief about transition probabilities between
                 a set of states that can vary between groups of observed transitions. For example,
                 when trying to understand human movement in a city, a hypothesis assuming
                 tourists to be more likely to move towards points of interests than locals, can be
                 shown to be more plausible with observed data than a hypothesis assuming the
                 opposite. Our approach incorporates these beliefs as Bayesian priors in a gen-
                 erative mixed transition Markov chain model, and compares their plausibility
                 utilizing Bayes factors. We discuss analytical and approximate inference for cal-
                 culating the marginal likelihoods for Bayes factors, give guidance on interpreting
                 the results, and illustrate our approach with several experiments on synthetic
                 and empirical data from Wikipedia and Flickr. Thus, this work enables a novel
                 kind of analysis for studying sequential data in many application areas.

                 References
                 1. Becker, M., Lemmerich, F., Singer, P., Strohmaier, M., Hotho, A.: Mixedtrails:
                    Bayesian hypothesis comparison on heterogeneous sequential data. Data Mining
                    and Knowledge Discovery (Jul 2017)




Copyright © 2017 by the paper’s authors. Copying permitted only for private and academic purposes.
In: M. Leyer (Ed.): Proceedings of the LWDA 2017 Workshops: KDML, FGWM, IR, and FGDB.
Rostock, Germany, 11.-13. September 2017, published at http://ceur-ws.org