We apply a cognitive modeling approach to the problem of measuring expertise on rank ordering problems. In these problems, people must order a set of items in terms of a given criterion (e.g., ordering American holidays through the calendar year). Using a cognitive model of behavior on this problem

search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. Here, we present results comparing Rank

We apply a cognitive modeling approach to the problem of measuring expertise on rank ordering tasks. In these tasks, people must order a set of items in terms of a given criterion. Using a cognitive model of behavior on this task that allows for individual differences in knowledge, we are able

This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task

as the task of ranking a given pool of captions. We introduce a new benchmark collection for sentence-based image description and search, consisting of 8,000 images that are each paired with five different captions which provide clear descriptions of the salient entities and events. We introduce a number

We apply a cognitive modeling approach to the problem of measuring expertise on rank ordering tasks. In these tasks, people must order a set of items in terms of a given criterion. Using a cognitive model of behavior on this task that allows for individual differences in knowledge, we are able

). With function approximation, two ways of formulating the agent's objective are useful. One is the average reward formulation, in which policies are ranked according to their long-term expected reward per step, ρ(π): where d π (s) = lim t→∞ P r {s t = s|s 0 , π} is the stationary distribution of states

A method is presented for ranking task-specific exposures using time-weighted average samples collected during the performance of multiple tasks. The task ranking can be used for purposes such as prioritizing further assessment or control. No a priori estimates of the individual task concentration

takes these features into account. We introduce a new method for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). We apply the boosting method to parsing the Wall Street Journal treebank. The method combined the log-likelihood under a baseline

the object. Given a set of seed features, we compute log likelihood ratios of class conditional sample densities from object and background to form a new set of candidate features tailored to the local object/background discrimination task. The two-class variance ratio is used to rank these new features