4,134 research outputs found
Learning to Rank Academic Experts in the DBLP Dataset
Expert finding is an information retrieval task that is concerned with the
search for the most knowledgeable people with respect to a specific topic, and
the search is based on documents that describe people's activities. The task
involves taking a user query as input and returning a list of people who are
sorted by their level of expertise with respect to the user query. Despite
recent interest in the area, the current state-of-the-art techniques lack in
principled approaches for optimally combining different sources of evidence.
This article proposes two frameworks for combining multiple estimators of
expertise. These estimators are derived from textual contents, from
graph-structure of the citation patterns for the community of experts, and from
profile information about the experts. More specifically, this article explores
the use of supervised learning to rank methods, as well as rank aggregation
approaches, for combing all of the estimators of expertise. Several supervised
learning algorithms, which are representative of the pointwise, pairwise and
listwise approaches, were tested, and various state-of-the-art data fusion
techniques were also explored for the rank aggregation framework. Experiments
that were performed on a dataset of academic publications from the Computer
Science domain attest the adequacy of the proposed approaches.Comment: Expert Systems, 2013. arXiv admin note: text overlap with
arXiv:1302.041
Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy
Expert finding is an information retrieval task concerned with the search for
the most knowledgeable people, in some topic, with basis on documents
describing peoples activities. The task involves taking a user query as input
and returning a list of people sorted by their level of expertise regarding the
user query. This paper introduces a novel approach for combining multiple
estimators of expertise based on a multisensor data fusion framework together
with the Dempster-Shafer theory of evidence and Shannon's entropy. More
specifically, we defined three sensors which detect heterogeneous information
derived from the textual contents, from the graph structure of the citation
patterns for the community of experts, and from profile information about the
academic experts. Given the evidences collected, each sensor may define
different candidates as experts and consequently do not agree in a final
ranking decision. To deal with these conflicts, we applied the Dempster-Shafer
theory of evidence combined with Shannon's Entropy formula to fuse this
information and come up with a more accurate and reliable final ranking list.
Experiments made over two datasets of academic publications from the Computer
Science domain attest for the adequacy of the proposed approach over the
traditional state of the art approaches. We also made experiments against
representative supervised state of the art algorithms. Results revealed that
the proposed method achieved a similar performance when compared to these
supervised techniques, confirming the capabilities of the proposed framework
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
ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks
Hash codes are efficient data representations for coping with the ever
growing amounts of data. In this paper, we introduce a random forest semantic
hashing scheme that embeds tiny convolutional neural networks (CNN) into
shallow random forests, with near-optimal information-theoretic code
aggregation among trees. We start with a simple hashing scheme, where random
trees in a forest act as hashing functions by setting `1' for the visited tree
leaf, and `0' for the rest. We show that traditional random forests fail to
generate hashes that preserve the underlying similarity between the trees,
rendering the random forests approach to hashing challenging. To address this,
we propose to first randomly group arriving classes at each tree split node
into two groups, obtaining a significantly simplified two-class classification
problem, which can be handled using a light-weight CNN weak learner. Such
random class grouping scheme enables code uniqueness by enforcing each class to
share its code with different classes in different trees. A non-conventional
low-rank loss is further adopted for the CNN weak learners to encourage code
consistency by minimizing intra-class variations and maximizing inter-class
distance for the two random class groups. Finally, we introduce an
information-theoretic approach for aggregating codes of individual trees into a
single hash code, producing a near-optimal unique hash for each class. The
proposed approach significantly outperforms state-of-the-art hashing methods
for image retrieval tasks on large-scale public datasets, while performing at
the level of other state-of-the-art image classification techniques while
utilizing a more compact and efficient scalable representation. This work
proposes a principled and robust procedure to train and deploy in parallel an
ensemble of light-weight CNNs, instead of simply going deeper.Comment: Accepted to ECCV 201
Divide and Fuse: A Re-ranking Approach for Person Re-identification
As re-ranking is a necessary procedure to boost person re-identification
(re-ID) performance on large-scale datasets, the diversity of feature becomes
crucial to person reID for its importance both on designing pedestrian
descriptions and re-ranking based on feature fusion. However, in many
circumstances, only one type of pedestrian feature is available. In this paper,
we propose a "Divide and use" re-ranking framework for person re-ID. It
exploits the diversity from different parts of a high-dimensional feature
vector for fusion-based re-ranking, while no other features are accessible.
Specifically, given an image, the extracted feature is divided into
sub-features. Then the contextual information of each sub-feature is
iteratively encoded into a new feature. Finally, the new features from the same
image are fused into one vector for re-ranking. Experimental results on two
person re-ID benchmarks demonstrate the effectiveness of the proposed
framework. Especially, our method outperforms the state-of-the-art on the
Market-1501 dataset.Comment: Accepted by BMVC201
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