17,241 research outputs found
Signed Distance-based Deep Memory Recommender
Personalized recommendation algorithms learn a user's preference for an item
by measuring a distance/similarity between them. However, some of the existing
recommendation models (e.g., matrix factorization) assume a linear relationship
between the user and item. This approach limits the capacity of recommender
systems, since the interactions between users and items in real-world
applications are much more complex than the linear relationship. To overcome
this limitation, in this paper, we design and propose a deep learning framework
called Signed Distance-based Deep Memory Recommender, which captures non-linear
relationships between users and items explicitly and implicitly, and work well
in both general recommendation task and shopping basket-based recommendation
task. Through an extensive empirical study on six real-world datasets in the
two recommendation tasks, our proposed approach achieved significant
improvement over ten state-of-the-art recommendation models
Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach
A significant amount of search queries originate from some real world
information need or tasks. In order to improve the search experience of the end
users, it is important to have accurate representations of tasks. As a result,
significant amount of research has been devoted to extracting proper
representations of tasks in order to enable search systems to help users
complete their tasks, as well as providing the end user with better query
suggestions, for better recommendations, for satisfaction prediction, and for
improved personalization in terms of tasks. Most existing task extraction
methodologies focus on representing tasks as flat structures. However, tasks
often tend to have multiple subtasks associated with them and a more
naturalistic representation of tasks would be in terms of a hierarchy, where
each task can be composed of multiple (sub)tasks. To this end, we propose an
efficient Bayesian nonparametric model for extracting hierarchies of such tasks
\& subtasks. We evaluate our method based on real world query log data both
through quantitative and crowdsourced experiments and highlight the importance
of considering task/subtask hierarchies.Comment: 10 pages. Accepted at SIGIR 2017 as a full pape
Next generation analytic tools for large scale genetic epidemiology studies of complex diseases
Over the past several years, genome‐wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled “Next Generation Analytic Tools for Large‐Scale Genetic Epidemiology Studies of Complex Diseases” on September 15–16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large‐scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene‐gene and gene‐environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized. Genet. Epidemiol . 36 : 22–35, 2012. © 2011 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93578/1/gepi20652.pd
COST Action TU 1406 quality specifications for roadway bridges (BridgeSpec)
Roadway bridges, being one of the most critical road infrastructures components, require regular maintenance actions. Therefore, it becomes important to define strategies to maximize societal benefits, derived from the investment made in these assets. Consequently, this investment should be planned, effectively managed and technically supported by appropriate management systems, supported in quality control plans. For this purpose, authorities need to produce an asset management plan which should, not only define the goals to be achieved by exploiting the roadway bridge network, but also identify the investment needs and priorities based on a life cycle cost criteria. Additionally, a proper condition assessment of these assets must be conducted to support the decision-making process regarding their preservation. A COST Action recently started in Europe with the aim of standardizing the establishment of quality control plans for roadway bridges. This paper describes this Action and, particularly, its most recent developments.
MaintenanceThis article is based upon work from COST Action TU-1406, Quality specifications for roadway bridges, standardization at a European level (BridgeSpec), supported by COST (European Cooperation in Science and Technology)
First impressions: A survey on vision-based apparent personality trait analysis
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft
Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application
Recommender systems (RSs) are systems that produce individualized recommendations as
output or drive the user in a personalized way to interesting or useful objects in a space of possible
options. Recently, RSs emerged as an effective support for decision making. However, when people make
decisions, they usually take into account different and often conicting information such as preferences,
long-term goals, context, and their current condition. This complexity is often ignored by RSs. In order to
provide an effective decision-making support, a RS should be ``holistic'', i.e., it should rely on a complete
representation of the user, encoding heterogeneous user features (such as personal interests, psychological
traits, health data, social connections) that may come from multiple data sources. However, to obtain such
holistic recommendations some steps are necessary: rst, we need to identify the goal of the decision-making
process; then, we have to exploit common-sense and domain knowledge to provide the user with the most
suitable suggestions that best t the recommendation scenario. In this article, we present a methodological
framework that can drive researchers and developers during the design process of this kind of ``holistic'' RS.
We also provide evidence of the framework validity by presenting the design process and the evaluation of
a food RS based on holistic principles
FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds
Users increasingly rely on social media feeds for consuming daily information. The items in a feed, such as news, questions, songs, etc., usually result from the complex interplay of a user's social contacts, her interests and her actions on the platform. The relationship of the user's own behavior and the received feed is often puzzling, and many users would like to have a clear explanation on why certain items were shown to them. Transparency and explainability are key concerns in the modern world of cognitive overload, filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, a framework that systematically discovers, ranks, and explains relationships between users' actions and items in their social media feeds. We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user. We posit that paths in this interaction graph connecting the user and her feed items can act as pertinent explanations for the user. These paths are scored with a learning-to-rank model that captures relevance and surprisal. User studies on two social platforms demonstrate the practical viability and user benefits of the FAIRY method
FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds
Users increasingly rely on social media feeds for consuming daily
information. The items in a feed, such as news, questions, songs, etc., usually
result from the complex interplay of a user's social contacts, her interests
and her actions on the platform. The relationship of the user's own behavior
and the received feed is often puzzling, and many users would like to have a
clear explanation on why certain items were shown to them. Transparency and
explainability are key concerns in the modern world of cognitive overload,
filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, a
framework that systematically discovers, ranks, and explains relationships
between users' actions and items in their social media feeds. We model the
user's local neighborhood on the platform as an interaction graph, a form of
heterogeneous information network constructed solely from information that is
easily accessible to the concerned user. We posit that paths in this
interaction graph connecting the user and her feed items can act as pertinent
explanations for the user. These paths are scored with a learning-to-rank model
that captures relevance and surprisal. User studies on two social platforms
demonstrate the practical viability and user benefits of the FAIRY method.Comment: WSDM 201
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