The task of expert finding has been getting increasing attention in information retrieval literature. However, the current state-of-the-art is still lacking in principled approaches for combining different sources of evidence. This paper explores the usage of unsupervised rank aggregation methods as a principled approach for combining multiple estimators of expertise, derived from the textual contents, from the graph-structure of the citation patterns for the community of experts, and from profile information about the experts. We specifically experimented two unsupervised rank aggregation approaches well known in the information retrieval literature, namely CombSUM and CombMNZ. Experiments made over a dataset of academic publications for the area of Computer Science attest for the adequacy of these methods.
Positional methods - For each object, we consider the preferences (i.e., the scores) given by each voter, aggregating them through some particular technique and finally re-ranking objects using the aggregated preferences. The first positional method was proposed by Borda, but linear and non-linear combinations of preferences, such as their arithmetic mean or the triangular norm, are also frequently used @cite_14 @cite_18 .
@cite_18 @cite_14
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Positional scoring rules in voting compute the score of an alternative by summing the scores for the alternative induced by every vote. This summation principle ensures that all votes contribute equally to the score of an alternative. We relax this assumption and, instead, aggregate scores by taking into account the rank of a score in the ordered list of scores obtained from the votes. This defines a new family of voting rules, rank-dependent scoring rules (RDSRs), based on ordered weighted average (OWA) operators, which, include all scoring rules, and many others, most of which of new. We study some properties of these rules, and show, empirically, that certain RDSRs are less manipulable than Borda voting, across a variety of statistical cultures. In this paper we present a general treatment of the preference aggregation problem, in which multiple preferences over objects must be combined into a single consensus ranking. We consider two instances of this problem: unsupervised aggregation where no information about a target ranking is available, and supervised aggregation where ground truth preferences are provided. For each problem class we develop novel learning methods that are applicable to a wide range of preference types. Specifically, for unsupervised aggregation we introduce the Multinomial Preference model (MPM) which uses a multinomial generative process to model the observed preferences. For the supervised problem we develop a supervised extension for MPM and then propose two fully supervised models. The first model employs SVD factorization to derive effective item features, transforming the aggregation problems into a learning-to-rank one. The second model aims to eliminate the costly SVD factorization and instantiates a probabilistic CRF framework, deriving unary and pairwise potentials directly from the observed preferences. Using a probabilistic framework allows us to directly optimize the expectation of any target metric, such as NDCG or ERR. All the proposed models operate on pairwise preferences and can thus be applied to a wide range of preference types. We empirically validate the models on rank aggregation and collaborative filtering data sets and demonstrate superior empirical accuracy. Hough voting methods efficiently handle the high complexity of multiscale, category-level object detection in cluttered scenes. The primary weakness of this approach is however that mutually dependent local observations are independently voting for intrinsically global object properties such as object scale. All the votes are added up to obtain object hypotheses. The assumption is thus that object hypotheses are a sum of independent part votes. Popular representation schemes are, however, based on an overlapping sampling of semi-local image features with large spatial support (e.g. SIFT or geometric blur). Features are thus mutually dependent and we incorporate these dependences into probabilistic Hough voting by presenting an objective function that combines three intimately related problems: i) grouping of mutually dependent parts, ii) solving the correspondence problem conjointly for dependent parts, and iii) finding concerted object hypotheses using extended groups rather than based on local observations alone. Experiments successfully demonstrate that state-of-the-art Hough voting and even sliding windows are significantly improved by utilizing part dependences and jointly optimizing groups, correspondences, and votes. We describe an approach to category-level detection and viewpoint estimation for rigid 3D objects from single 2D images. In contrast to many existing methods, we directly integrate 3D reasoning with an appearance-based voting architecture. Our method relies on a nonparametric representation of a joint distribution of shape and appearance of the object class. Our voting method employs a novel parametrization of joint detection and viewpoint hypothesis space, allowing efficient accumulation of evidence. We combine this with a re-scoring and refinement mechanism, using an ensemble of view-specific Support Vector Machines. We evaluate the performance of our approach in detection and pose estimation of cars on a number of benchmark datasets.