Comparing Sequential Hypotheses with HypTrails

Transcript

1. Part 4 Comparing Hypotheses about Sequential Data

2. 2 Example: Human Navigation • Humans prefer to navigate… –

   H1: over semantically similar websites – H2: via self-loops (e.g., refreshing) – H3: by using the structural link network – H4: by preferring similar categories – H5: by utilizing structural properties – H6: by information scent [West et al. IJCAI 2009], [Singer et al. IJSWIS 2013], [West & Leskovec WWW 2012], [Chi et al. CHI 2001]

3. 3 Example: Human Navigation • Humans prefer to navigate… –

   H1: over semantically similar websites – H2: via self-loops (e.g., refreshing) – H3: by using the structural link network – H4: by preferring similar categories – H5: by utilizing structural properties – H6: by information scent [West et al. IJCAI 2009], [Singer et al. IJSWIS 2013], [West & Leskovec WWW 2012], [Chi et al. CHI 2001] What is the relative plausibility of these hypotheses given data?

4. 4 Example: Human Navigation • Humans prefer to navigate… –

   H1: over semantically similar websites – H2: via self-loops (e.g., refreshing) – H3: by using the structural link network – H4: by preferring similar categories – H5: by utilizing structural properties – H6: by information scent [West et al. IJCAI 2009], [Singer et al. IJSWIS 2013], [West & Leskovec WWW 2012], [Chi et al. CHI 2001] HypTrails [Singer et al. WWW 2015]

5. 5 HypTrails in a nutshell • Goal: Express and compare

   hypotheses about sequences in a coherent research approach • Method: – First-order Markov chain model – Bayesian inference • Idea: – Incorporate hypotheses as priors – Utilize sensitivity of marginal likelihood on the prior • Outcome: Partial ordering of hypotheses

6. Structure of HypTrails

7. 7 Structure of HypTrails MC Model MC Model S1 S1

   S2 S2 S3 S3 1/2 1/2 1/3 2/3 1

8. How to express hypotheses?

9. How to express hypotheses? As assumptions in parameters of Markov

   Chain model.

10. 10 Structural hypothesis 1/3 1 1/3 1 1/3

11. 11 Uniform hypothesis 1/3

12. 12 Structure of HypTrails MC Model MC Model Hypothesis (H1)

    Hypothesis (H1) Belief in parameters 1 2 3 0.00 0.33 0.00 h 3 1.00 0.33 1.00 h 2 0.00 0.33 0.00 h 1 h 3 h 2 h 1 0.00 0.99 0.00 h 3 3.01 0.99 3.01 h 2 0.00 0.99 0.00 h 1 h 3 h 2 h 1 Belief in parameters

13. 13 Empirical observations 1.0 2/3 1/3 1

14. 14 Which hypothesis is the most plausible one?

15. 15 Bayesian model comparison: marginal likelihood

16. 16 Bayesian model comparison: marginal likelihood Probability of parameters before

    observing data

17. 17 Bayesian model comparison: marginal likelihood Probability of parameters before

    observing data Hypothesis

18. 18 Structure of HypTrails MC Model MC Model Hypothesis (H1)

    Hypothesis (H1) Belief in parameters Prior (H1) Prior (H1) Elicitation Data (Trails) Data (Trails) Marginal likelihood (H1) Marginal likelihood (H1) Influence Influence

19. 19 How to elicit priors from expressed hypotheses?

20. 20 Conjugate Dirichlet prior • Hyperparameters: pseudo counts

21. 21 Conjugate Dirichlet prior • Hyperparameters: pseudo counts Hypothesis parameters

    Dirichlet hyperparameters

22. 22 Elicitation • Multiply row-normalized hypothesis matrix with concentration parameter

    k • Higher k → stronger belief • Additional proto-prior Hypothesis parameters Dirichlet hyperparameters

23. 23 2 state example: Beta prior Hypothesis:

24. 24 2 state example: Beta prior Hypothesis: k = 0

25. 25 2 state example: Beta prior Hypothesis: k = 1

26. 26 2 state example: Beta prior Hypothesis: k = 10

27. 27 2 state example: Beta prior Hypothesis: k = 100

28. 28 Example: Structural hypothesis proto prior

29. 29 Structure of HypTrails MC Model MC Model Hypothesis (H1)

    Hypothesis (H1) Dirichlet Prior (H1) Dirichlet Prior (H1) Data (Trails) Data (Trails) Marginal likelihood (H1) Marginal likelihood (H1) Hypothesis (H2) Hypothesis (H2) Dirichlet Prior (H2) Dirichlet Prior (H2) Marginal likelihood (H2) Marginal likelihood (H2) Compare Belief in parameters Elicitation Influence Influence

30. 30 Example result: Last.fm 0 1 2 3 4 hypothesis

    weighting factor k −1.55 −1.50 −1.45 −1.40 −1.35 −1.30 −1.25 −1.20 −1.15 −1.10 evidence 1e5 uniform self-loop track date similarity Higher plausibility Higher belief

31. 31 Example result: Last.fm 0 1 2 3 4 hypothesis

    weighting factor k −1.55 −1.50 −1.45 −1.40 −1.35 −1.30 −1.25 −1.20 −1.15 −1.10 evidence 1e5 uniform self-loop track date similarity

32. Hands-on jupyter notebook

33. 33 Further applications • Ontology engineering – edit sequences [Walk

    et al. ISWC 2015] • Real-world navigational trails – Flickr [Becker et al. SocialCom 2015] – Taxi data [Espín-Noboa et al. WWW 2016] – Car data [Atzmüller et al. WWW 2016] • Wikipedia co-editing patterns [Samoilenko et al. 2016]

34. 34 Methodological extensions • Detect and model heterogeneity in data

    • Higher-order Markov chain models • Adaption for other models

35. 35 What have we learned? • Comparing hypotheses about sequential

    data • Bayesian approach: HypTrails • Applications

36. Questions?

37. for your attention! T T H H A A N

    N K K S S @ph_singer www.philippsinger.info florian.lemmerich.net

38. 38 References 1/2 [West et al. WWW 2015] Robert West,

    Ashwin Paranjape, and Jure Leskovec: Mining Missing Hyperlinks from Human Navigation Traces: A Case Study of Wikipedia. 24th International World Wide Web Conference (WWW'15), Florence, Italy, 2015. [Singer et al. IJSWIS 2013] Philipp Singer, Thomas Niebler, Markus Strohmaier and Andreas Hotho, Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia, International Journal on Semantic Web and Information Systems (IJSWIS), vol 9(4), 41-70, 2013 [West & Leskovec WWW 2012] Robert West and Jure Leskovec: Human Wayfinding in Information Networks 21st International World Wide Web Conference (WWW'12), pp. 619–628, Lyon, France, 2012. [Chi et al. CHI 2001] Chi, Ed H., et al. "Using information scent to model user information needs and actions and the Web." Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 2001. [Singer et al. WWW 2015] Singer, P., Helic, D., Hotho, A., and 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. [Walk et al. ISWC 2015] Simon Walk, Philipp Singer, Lisette Espín Noboa, Tania Tudorache, Mark A. Musen and Markus Strohmaier, Understanding How Users Edit Ontologies: Comparing Hypotheses About Four Real-World Projects, 14th International Semantic Web Conference, Betlehem, Pennsylvania, USA, 2015

39. 39 References 2/2 [Becker et al. SocialCom 2015] Martin Becker,

    Philipp Singer, Florian Lemmerich, Andreas Hotho, Denis Helic and Markus Strohmaier, Photowalking the City: Comparing Hypotheses About Urban Photo Trails on Flickr, 7th International Conference on Social Informatics, Beijing, China, 2015 [Espín-Noboa et al. WWW 2016] Lisette Espín-Noboa, Florian Lemmerich, Philipp Singer and Markus Strohmaier, Discovering and Characterizing Mobility Patterns in Urban Spaces: A Study of Manhattan Taxi Data, 6th International Workshop on Location and the Web at WWW2016, Montreal, Canada, 2016 [Samoilenko et al. 2016] Samoilenko, A., Karimi, F., Edler, D., Kunegis, J., & Strohmaier, M. (2016). Linguistic neighbourhoods: explaining cultural borders on Wikipedia through multilingual co-editing activity. EPJ Data Science, 5(1), 1., 2016 [Atzmüller et al. WWW 2016] Atzmueller, M., Schmidt, A., & Kibanov, M. (2016). DASHTrails: An Approach for Modeling and Analysis of Distribution-Adapted Sequential Hypotheses and Trails. In Proceedings of the World Wide Web Conference Companion, 2016