601 research outputs found
Fixed-Parameter Algorithms for Computing Kemeny Scores - Theory and Practice
The central problem in this work is to compute a ranking of a set of elements
which is "closest to" a given set of input rankings of the elements. We define
"closest to" in an established way as having the minimum sum of Kendall-Tau
distances to each input ranking. Unfortunately, the resulting problem Kemeny
consensus is NP-hard for instances with n input rankings, n being an even
integer greater than three. Nevertheless this problem plays a central role in
many rank aggregation problems. It was shown that one can compute the
corresponding Kemeny consensus list in f(k) + poly(n) time, being f(k) a
computable function in one of the parameters "score of the consensus", "maximum
distance between two input rankings", "number of candidates" and "average
pairwise Kendall-Tau distance" and poly(n) a polynomial in the input size. This
work will demonstrate the practical usefulness of the corresponding algorithms
by applying them to randomly generated and several real-world data. Thus, we
show that these fixed-parameter algorithms are not only of theoretical
interest. In a more theoretical part of this work we will develop an improved
fixed-parameter algorithm for the parameter "score of the consensus" having a
better upper bound for the running time than previous algorithms.Comment: Studienarbei
Parameterized Algorithmics for Computational Social Choice: Nine Research Challenges
Computational Social Choice is an interdisciplinary research area involving
Economics, Political Science, and Social Science on the one side, and
Mathematics and Computer Science (including Artificial Intelligence and
Multiagent Systems) on the other side. Typical computational problems studied
in this field include the vulnerability of voting procedures against attacks,
or preference aggregation in multi-agent systems. Parameterized Algorithmics is
a subfield of Theoretical Computer Science seeking to exploit meaningful
problem-specific parameters in order to identify tractable special cases of in
general computationally hard problems. In this paper, we propose nine of our
favorite research challenges concerning the parameterized complexity of
problems appearing in this context
Unsupervised Graph-based Rank Aggregation for Improved Retrieval
This paper presents a robust and comprehensive graph-based rank aggregation
approach, used to combine results of isolated ranker models in retrieval tasks.
The method follows an unsupervised scheme, which is independent of how the
isolated ranks are formulated. Our approach is able to combine arbitrary
models, defined in terms of different ranking criteria, such as those based on
textual, image or hybrid content representations.
We reformulate the ad-hoc retrieval problem as a document retrieval based on
fusion graphs, which we propose as a new unified representation model capable
of merging multiple ranks and expressing inter-relationships of retrieval
results automatically. By doing so, we claim that the retrieval system can
benefit from learning the manifold structure of datasets, thus leading to more
effective results. Another contribution is that our graph-based aggregation
formulation, unlike existing approaches, allows for encapsulating contextual
information encoded from multiple ranks, which can be directly used for
ranking, without further computations and post-processing steps over the
graphs. Based on the graphs, a novel similarity retrieval score is formulated
using an efficient computation of minimum common subgraphs. Finally, another
benefit over existing approaches is the absence of hyperparameters.
A comprehensive experimental evaluation was conducted considering diverse
well-known public datasets, composed of textual, image, and multimodal
documents. Performed experiments demonstrate that our method reaches top
performance, yielding better effectiveness scores than state-of-the-art
baseline methods and promoting large gains over the rankers being fused, thus
demonstrating the successful capability of the proposal in representing queries
based on a unified graph-based model of rank fusions
- …