2,808 research outputs found
GraphMaps: Browsing Large Graphs as Interactive Maps
Algorithms for laying out large graphs have seen significant progress in the
past decade. However, browsing large graphs remains a challenge. Rendering
thousands of graphical elements at once often results in a cluttered image, and
navigating these elements naively can cause disorientation. To address this
challenge we propose a method called GraphMaps, mimicking the browsing
experience of online geographic maps.
GraphMaps creates a sequence of layers, where each layer refines the previous
one. During graph browsing, GraphMaps chooses the layer corresponding to the
zoom level, and renders only those entities of the layer that intersect the
current viewport. The result is that, regardless of the graph size, the number
of entities rendered at each view does not exceed a predefined threshold, yet
all graph elements can be explored by the standard zoom and pan operations.
GraphMaps preprocesses a graph in such a way that during browsing, the
geometry of the entities is stable, and the viewer is responsive. Our case
studies indicate that GraphMaps is useful in gaining an overview of a large
graph, and also in exploring a graph on a finer level of detail.Comment: submitted to GD 201
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
Modern recommender systems model people and items by discovering or `teasing
apart' the underlying dimensions that encode the properties of items and users'
preferences toward them. Critically, such dimensions are uncovered based on
user feedback, often in implicit form (such as purchase histories, browsing
logs, etc.); in addition, some recommender systems make use of side
information, such as product attributes, temporal information, or review text.
However one important feature that is typically ignored by existing
personalized recommendation and ranking methods is the visual appearance of the
items being considered. In this paper we propose a scalable factorization model
to incorporate visual signals into predictors of people's opinions, which we
apply to a selection of large, real-world datasets. We make use of visual
features extracted from product images using (pre-trained) deep networks, on
top of which we learn an additional layer that uncovers the visual dimensions
that best explain the variation in people's feedback. This not only leads to
significantly more accurate personalized ranking methods, but also helps to
alleviate cold start issues, and qualitatively to analyze the visual dimensions
that influence people's opinions.Comment: AAAI'1
Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems
Designing an e-commerce recommender system that serves hundreds of millions
of active users is a daunting challenge. From a human vision perspective,
there're two key factors that affect users' behaviors: items' attractiveness
and their matching degree with users' interests. This paper proposes Telepath,
a vision-based bionic recommender system model, which understands users from
such perspective. Telepath is a combination of a convolutional neural network
(CNN), a recurrent neural network (RNN) and deep neural networks (DNNs). Its
CNN subnetwork simulates the human vision system to extract key visual signals
of items' attractiveness and generate corresponding activations. Its RNN and
DNN subnetworks simulate cerebral cortex to understand users' interest based on
the activations generated from browsed items. In practice, the Telepath model
has been launched to JD's recommender system and advertising system. For one of
the major item recommendation blocks on the JD app, click-through rate (CTR),
gross merchandise value (GMV) and orders have increased 1.59%, 8.16% and 8.71%
respectively. For several major ads publishers of JD demand-side platform, CTR,
GMV and return on investment have increased 6.58%, 61.72% and 65.57%
respectively by the first launch, and further increased 2.95%, 41.75% and
41.37% respectively by the second launch.Comment: 8 pages, 11 figures, 1 tabl
VAS (Visual Analysis System): An information visualization engine to interpret World Wide Web structure
People increasingly encounter problems of interpreting and filtering mass quantities of information. The enormous growth of information systems on the World Wide Web has demonstrated that we need systems to filter, interpret, organize and present information in ways that allow users to use these large quantities of information. People need to be able to extract knowledge from this sometimes meaningful but sometimes useless mass of data in order to make informed decisions. Web users need to have some kind of information about the sort of page they might visit, such as, is it a rarely referenced or often-referenced page? This master\u27s thesis presents a method to address these problems using data mining and information visualization techniques
Evaluation campaigns and TRECVid
The TREC Video Retrieval Evaluation (TRECVid) is an
international benchmarking activity to encourage research
in video information retrieval by providing a large test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results. TRECVid completed its fifth annual cycle at the end of 2005 and in 2006 TRECVid will involve almost 70 research organizations, universities and other consortia. Throughout its existence, TRECVid has benchmarked both interactive and automatic/manual searching for shots from within a video
corpus, automatic detection of a variety of semantic and
low-level video features, shot boundary detection and the
detection of story boundaries in broadcast TV news. This
paper will give an introduction to information retrieval (IR) evaluation from both a user and a system perspective, highlighting that system evaluation is by far the most prevalent type of evaluation carried out. We also include a summary of TRECVid as an example of a system evaluation benchmarking campaign and this allows us to discuss whether
such campaigns are a good thing or a bad thing. There are
arguments for and against these campaigns and we present
some of them in the paper concluding that on balance they
have had a very positive impact on research progress
Linked open graph: Browsing multiple SPARQL entry points to build your own LOD views
AbstractA number of accessible RDF stores are populating the linked open data world. The navigation on data reticular relationships is becoming every day more relevant. Several knowledge base present relevant links to common vocabularies while many others are going to be discovered increasing the reasoning capabilities of our knowledge base applications. In this paper, the Linked Open Graph, LOG, is presented. It is a web tool for collaborative browsing and navigation on multiple SPARQL entry points. The paper presented an overview of major problems to be addressed, a comparison with the state of the arts tools, and some details about the LOG graph computation to cope with high complexity of large Linked Open Dada graphs. The LOG.disit.org tool is also presented by means of a set of examples involving multiple RDF stores and putting in evidence the new provided features and advantages using dbPedia, Getty, Europeana, Geonames, etc. The LOG tool is free to be used, and it has been adopted, developed and/or improved in multiple projects: such as ECLAP for social media cultural heritage, Sii-Mobility for smart city, and ICARO for cloud ontology analysis, OSIM for competence/knowledge mining and analysis
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