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Wednesday, April 5, 2017

Week 12: FolkTrails: Interpreting Navigation Behavior in a Social Tagging System

Motivation: Social tagging systems have been widely used to organize and store online information, such as webpages and publications (Delicious and Bibsonomy). In such systems, users can freely assign keywords to specific resources for retrieval and organization in the future. By tracing users' behaviors in those tagging systems, researchers are able to understand how they assign tags, navigate among resources and consume information. This paper focuses on the specific problem of interpreting human navigation behaviors within Bibsonomy, and exploring the behavioral differences between different user subgroups.

Methods: It formulates a set of hypotheses which can be encoded as transition probability, and then apply HypTrails [1], a framework for comparing hypotheses based on empirical observations, to figure out which hypotheses better capture the intrinsic mechanism of human navigation trails. Figure 1 shows an example of user reviews for restaurants in Italy on Yelp. To be specific, HypTrails defines a navigation trail as a first-order Markov chain over a sequence of states, so that each hypothesis can be formulated as transition probabilities jumping from one state to another. To conclude which hypothesis better reflect empirical observations, it leverages Bayes factor, which compares the marginal likelihood P(D|H) where D represents real observations and H indicates hypothesis.

Figure 1. (a-c) show three hypotheses about human trails - uniform hypothesis, geo hypothesis and self-loop hypothesis, (d) reveals the empirical observation.
In this paper, six basic hypotheses are formulated:

  1. Uniform Hypothesis - user randomly select a page for next visit
  2. Page Consistent Hypothesis - user visits the same page in next step (due to pagination)
  3. Category Consistent Hypothesis - user visits page under the same category as current page
  4. User Consistent Hypothesis - a transition's target and source belong to the same user
  5. Folksonomy Consistent Hypothesis - user goes to a page by following folksonomy structure
  6. Semantic Navigation Hypothesis - user goes to a page which maintains semantic relations with current one

Three combined hypotheses are introduced:

  1. Folksonomy Consistent & Semantic Navigation Hypothesis
  2. User Consistent & Semantic Navigation Hypothesis
  3. User Consistent & Folksonomy Navigation Hypothesis

Experiment results: The above mentioned hypotheses are tested based on empirical dataset collected from the social tagging system Bibsonomy. Results show that (i) in overall, the combination of user consistent & semantic navigation hypothesis works best, and the second one is user consistent hypothesis; (ii) users show different browsing behaviors between within his/her resources and outside his/her resources - within navigations tend to be explained by semantic navigation hypothesis, while outside behaviors are likely to be following folksonomy structure; (iii) short-term and long-term users show different behaviors - short-term users are likely to follow semantic navigation while long-term users are prone to follow folksonomy structure.

[1]  Singer, P., Helic, D., Hotho, A., Strohmaier, M.: HypTrails: A bayesian approach for comparing hypotheses about human trails on the Web. In: Proc. of the 24th WWW Conf. (2015) 

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