Markov Layout

Abstract

Consider the problem of laying out a set of n images that match a query onto the nodes of a √ n× √ n grid. We are given a score for each image, as well as the distribution of patterns by which a user's eye scans the nodes of the grid and we wish to maximize the expected total score of images selected by the user. This is a special case of the Markov layout problem, in which we are given a Markov chain M together with a set of objects to be placed at the states of the Markov chain. Each object has a utility to the user if viewed, as well as a stopping probability with which the user ceases to look further at objects. This layout problem is prototypical in a number of applications in web search and advertising, particularly in an emerging genre of search results pages from major engines. In a different class of applications, the states of the Markov chain are web pages at a publishers website and the objects are advertisements.We study the approximability of the Markov layout problem. Our main result is an O(log n) approximation algorithm for the most general version of the problem. The core idea is to transform an optimization problem over partial permutations into an optimization problem over sets by losing a logarithmic factor in approximation; the latter problem is then shown to be submodular with two matroid constraints, which admits a constant-factor approximation. In contrast, we also show the problem is APX-hard via a reduction from CUBIC MAX-BISECTION.We then study harder variants of greater practical interest of the problem in which no gaps -states of M with no object placed on them -are allowed. By exploiting the geometry, we obtain an O(log 3/2 n) approximation algorithm when the digraph underlying M is a grid and an O(log n) approximation algorithm when it is a tree. These special cases are especially appropriate for our applications.

Repetition-aware content placement in navigational networks

“…Navigational networks: More recent work on node -selection in online navigational networks includes the work of Chierichetti et al [8] and Charikar et al [6]. In their work Charikar et al [6] assume a similar propagation model and objective to ours, in that users traverse a Markov chain and the ultimate goal is to assign content to certain states in this chain.…”

“…Contributions: Problems of picking important (or target) nodes in networks have been studied extensively in the past, both in information-flow (or social) networks [7,9,11,16], in transportation [2,3,13,14,20] and in navigational networks [6,8]. A thorough examination of this related work is presented in Section 2.…”

“…Chierichetti et al [8] assume a user-navigation model similar to ours and their goal is to find an optimal placement of online advertisements. In their setting, ads are placed to the nodes of the navigation network and there is a utility assigned to a user seeing an ad, that depends both on the state and the specific ad that is shown.…”