508 research outputs found
Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016
© 2017 The journal Knowledge-based Systems (KnoSys) has been published for over 25 years, during which time its main foci have been extended to a broad range of studies in computer science and artificial intelligence. Answering the questions: “What is the KnoSys community interested in?” and “How does such interest change over time?” are important to both the editorial board and audience of KnoSys. This paper conducts a topic-based bibliometric study to detect and predict the topic changes of KnoSys from 1991 to 2016. A Latent Dirichlet Allocation model is used to profile the hotspots of KnoSys and predict possible future trends from a probabilistic perspective. A model of scientific evolutionary pathways applies a learning-based process to detect the topic changes of KnoSys in sequential time slices. Six main research areas of KnoSys are identified, i.e., expert systems, machine learning, data mining, decision making, optimization, and fuzzy, and the results also indicate that the interest of KnoSys communities in the area of computational intelligence is raised, and the ability to construct practical systems through knowledge use and accurate prediction models is highly emphasized. Such empirical insights can be used as a guide for KnoSys submissions
Graph Neural Network for Service Recommender System in Digital Service Marketplace
The emergence of the platform economy has resulted in the decline of many traditional forms of doing business. Freelance work makes use of a platform to connect businesses or people with other businesses or persons in order to solve particular issues or deliver specific services in return for payment. The pairing process involves a buyer that needs work done, a platform that handles the algorithm, and a worker who is willing to do the job via the platform. This research argues that by efficiently pairing the talents of workers to the requirements of buyers, the platforms have the ability to expedite business operations for buyers, empower platform workers, and significantly improve the overall customer experience. Graph Convolutional Networks (GCNs) are inspired by CNNs and aim to expand the convolution operation from grid records to graph records, which in turn facilitates advances in the graph domain. In order to develop reliable and accurate embeddings for digital service recommendation, we employed a graph-based technique on a freelance platform dataset using the graph linkages of services and buyer data. We employed an aggregation-based inductive graph convolution network, namely, Graph SAmple and aggreGatE (GraphSAGE). It is a generalized inductive architecture that learns to construct embeddings for previously unknown data by sampling and combining attributes from a node's immediate neighborhood. We also applied PinSage, a stochastic Graph Convolutional Network (GCN) that can learn node embeddings in platform networks with many digital services. When a robust recommender system is used in digital service marketplace, it can offer promising results that may increase users' satisfaction with the service and boost the platform's ability to increase revenue
The Users' Perspective on the Privacy-Utility Trade-offs in Health Recommender Systems
Privacy is a major good for users of personalized services such as
recommender systems. When applied to the field of health informatics, privacy
concerns of users may be amplified, but the possible utility of such services
is also high. Despite availability of technologies such as k-anonymity,
differential privacy, privacy-aware recommendation, and personalized privacy
trade-offs, little research has been conducted on the users' willingness to
share health data for usage in such systems. In two conjoint-decision studies
(sample size n=521), we investigate importance and utility of
privacy-preserving techniques related to sharing of personal health data for
k-anonymity and differential privacy. Users were asked to pick a preferred
sharing scenario depending on the recipient of the data, the benefit of sharing
data, the type of data, and the parameterized privacy. Users disagreed with
sharing data for commercial purposes regarding mental illnesses and with high
de-anonymization risks but showed little concern when data is used for
scientific purposes and is related to physical illnesses. Suggestions for
health recommender system development are derived from the findings.Comment: 32 pages, 12 figure
Rough Set Based Rule Evaluations and Their Applications
Knowledge discovery is an important process in data analysis, data
mining and machine learning. Typically knowledge is presented in the
form of rules. However, knowledge discovery systems often generate a
huge amount of rules. One of the challenges we face is how to
automatically discover interesting and meaningful knowledge from
such discovered rules. It is infeasible for human beings to select
important and interesting rules manually. How to provide a measure
to evaluate the qualities of rules in order to facilitate the
understanding of data mining results becomes our focus. In this
thesis, we present a series of rule evaluation techniques for the
purpose of facilitating the knowledge understanding process. These
evaluation techniques help not only to reduce the number of rules,
but also to extract higher quality rules. Empirical studies on both
artificial data sets and real world data sets demonstrate how such
techniques can contribute to practical systems such as ones for
medical diagnosis and web personalization.
In the first part of this thesis, we discuss several rule evaluation
techniques that are proposed towards rule postprocessing. We show
how properly defined rule templates can be used as a rule evaluation
approach. We propose two rough set based measures, a Rule Importance
Measure, and a Rules-As-Attributes Measure,
%a measure of considering rules as attributes,
to rank the important and interesting rules. In the second part of
this thesis, we show how data preprocessing can help with rule
evaluation. Because well preprocessed data is essential for
important rule generation, we propose a new approach for processing
missing attribute values for enhancing the generated rules. In the
third part of this thesis, a rough set based rule evaluation system
is demonstrated to show the effectiveness of the measures proposed
in this thesis. Furthermore, a new user-centric web personalization
system is used as a case study to demonstrate how the proposed
evaluation measures can be used in an actual application
Integrating Wearable Devices and Recommendation System: Towards a Next Generation Healthcare Service Delivery
Researchers have identified lifestyle diseases as a major threat to human civilization. These diseases gradually progress without giving any warning and result in a sudden health aggravation that leads to a medical emergency. As such, individuals can only avoid the life-threatening condition if they regularly monitor their health status. Health recommendation systems allow users to continuously monitor their health and deliver proper health advice to them. Also, continuous health monitoring depends on the real-time data exchange between health solution providers and users. In this regard, healthcare providers have begun to use wearable devices and recommendation systems to collect data in real time and to manage health conditions based on the generated data. However, we lack literature that has examined how individuals use wearable devices, what type of data the devices collect, and how providers use the data for delivering solutions to users. Thus, we decided to explore the available literature in this domain to understand how wearable devices can provide solutions to consumers. We also extended our focus to cover current health service delivery frameworks with the help of recommender systems. Thus, this study reviews health-monitoring services by conglomerating both wearable device and recommendation system to come up with personalized health and fitness solutions. Additionally, the paper elucidates key components of an advanced-level real-time monitoring service framework to guide future research and practice in this domain
Efficient Decision Support Systems
This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers
Preferences in Case-Based Reasoning
Case-based reasoning (CBR) is a well-established problem solving paradigm
that has been used in a wide range of real-world applications. Despite
its great practical success, work on the theoretical foundations of CBR is
still under way, and a coherent and universally applicable methodological
framework is yet missing. The absence of such a framework inspired the
motivation for the work developed in this thesis. Drawing on recent research
on preference handling in Artificial Intelligence and related fields, the goal of
this work is to develop a well theoretically-founded framework on the basis
of formal concepts and methods for knowledge representation and reasoning
with preferences
Workshop NotesInternational Workshop ``What can FCA do for Artificial Intelligence?'' (FCA4AI 2015)
International audienceThis volume includes the proceedings of the fourth edition of the FCA4AI --What can FCA do for Artificial Intelligence?-- Workshop co-located with the IJCAI 2015 Conference in Buenos Aires (Argentina). Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification. FCA allows one to build a concept lattice and a system of dependencies (implications) which can be used for many AI needs, e.g. knowledge discovery, learning, knowledge representation, reasoning, ontology engineering, as well as information retrieval and text processing. There are many ``natural links'' between FCA and AI, and the present workshop is organized for discussing about these links and more generally for improving the links between knowledge discovery based on FCA and knowledge management in artificial intelligence
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