Name
Abstract | ||
|---|---|---|
While a plethora of hypertext links exist on the Web, only a small amount of them are regularly clicked. Starting from this observation, we set out to study large-scale click data from Wikipedia in order to understand what makes a link successful. We systematically analyze effects of link properties on the popularity of links. By utilizing mixed-effects hurdle models supplemented with descriptive insights, we find evidence of user preference towards links leading to the periphery of the network, towards links leading to semantically similar articles, and towards links in the top and left-side of the screen. We integrate these findings as Bayesian priors into a navigational Markov chain model and by doing so successfully improve the model fits. We further adapt and improve the well-known classic PageRank algorithm that assumes random navigation by accounting for observed navigational preferences of users in a weighted variation. This work facilitates understanding navigational click behavior and thus can contribute to improving link structures and algorithms utilizing these structures. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3038912.3052613 | Proceedings of the 26th International Conference on World Wide Web |
Keywords | DocType | ISBN |
Human Click Behavior, Navigation, Wikipedia | Conference | 978-1-4503-4913-0 |
Citations | PageRank | References |
9 | 0.54 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dimitar Dimitrov | 1 | 376 | 49.21 |
| Philipp Singer | 2 | 253 | 24.15 |
| Florian Lemmerich | 3 | 241 | 27.41 |
| Markus Strohmaier | 4 | 1210 | 102.65 |