# HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web

@article{Singer2015HypTrailsAB, title={HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web}, author={Philipp Singer and D. Helic and Andreas Hotho and Markus Strohmaier}, journal={Proceedings of the 24th International Conference on World Wide Web}, year={2015} }

When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses…

## 67 Citations

A Bayesian Method for Comparing Hypotheses About Human Trails

- Computer ScienceACM Trans. Web
- 2017

This work presents a method called HypTrails, which utilizes Markov chain models with Bayesian inference for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states.

Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications

- Computer ScienceECML/PKDD
- 2017

This talk wants to give an introduction to HypTrails and showcase selected real-world applications on urban mobility and reading behavior on Wikipedia.

MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data

- Computer ScienceData Mining and Knowledge Discovery
- 2017

This work proposes MixedTrails, a Bayesian approach for comparing the plausibility of hypotheses regarding the generative processes of heterogeneous sequence data, and incorporates such hypotheses as Bayesian priors in a generative mixed transition Markov chain model, and compares their plausibility utilizing Bayes factors.

What Makes a Link Successful on Wikipedia?

- Computer ScienceWWW
- 2017

The well-known classic PageRank algorithm is adapted and improved that assumes random navigation by accounting for observed navigational preferences of users in a weighted variation to facilitate understanding navigational click behavior.

MicroTrails: comparing hypotheses about task selection on a crowdsourcing platform

- Computer ScienceI-KNOW
- 2015

The approach enables crowdsourcing companies to better understand their users in order to optimize their platforms, e.g., by incorparting the gained knowledge about these factors into task recommentation systems.

Data-driven modelling and probabilistic analysis of interactive software usage

- Computer ScienceJ. Log. Algebraic Methods Program.
- 2018

Analyzing Sequential User Behavior on the Web

- Computer ScienceWWW
- 2016

Fundamental methods for studying categorical sequences on the Web focus on sequential pattern mining, modeling and inference aiming at better understanding the production of sequences.

Di erent Topic , Di erent Tra ic : How Search and Navigation Interplay on Wikipedia

- Computer Science
- 2019

This work studies access behavior by employing two main metrics, namely searchshare – the relative amount of views an article received by search – and resistance – the ability of an article to relay trac to other Wikipedia articles – to characterize articles.

JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs

- Computer ScienceAppl. Netw. Sci.
- 2017

This work extends this existing arsenal of methods with JANUS, a hypothesis-driven Bayesian approach that allows to intuitively compare hypotheses about edge formation in multigraphs, and proposes to express hypotheses as priors encoding the authors' belief about parameters.

Retrospective Higher-Order Markov Processes for User Trails

- Computer ScienceKDD
- 2017

The retrospective higher-order Markov process (RHOMP) is proposed as a low-parameter model for such sequences of data where the transitions depend retrospectively on a single history state instead of an arbitrary combination of history states.

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