ChoiceRank: Identifying Preferences from Node Traffic in Networks

Consider the problem of estimating click probabilities for links between pages of a website, given a hyperlink graph and aggregate statistics on the number of times each page has been visited. Naively, one might expect that the probability of clicking on a particular link should be roughly proportional to the traffic of the link's target. However, this neglects important structural effects: a page's traffic is influenced by a) the number of incoming links, b) the traffic at the pages that link to it, and c) the traffic absorbed by competing links. In order to successfully infer click probabilities, it is therefore necessary to disentangle the preference for a page (i.e., the intrinsic propensity of a user to click on a link pointing to it) from the page's visibility (the exposure it gets from pages linking to it). Building upon recent work by Kumar et al. [2015], we present a statistical framework that tackles a general formulation of the problem: given a network (representing possible transitions between nodes) and the marginal traffic at each node, recover the transition probabilities.