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2022 | Book

Intelligent Processing Practices and Tools for E-Commerce Data, Information, and Knowledge

Editors: Honghao Gao, Prof. Jung Yoon Kim, Walayat Hussain, Dr. Muddesar Iqbal, Yucong Duan

Publisher: Springer International Publishing

Book Series: EAI/Springer Innovations in Communication and Computing


About this book

This book discusses recent research and applications about intelligent processing practices and tools for e-commerce data, information and knowledge. The authors first explain how advances in intelligent processing of data, information and knowledge that has wildly been used in e-commerce applications. They then show how this brings new opportunities and challenges for processing e-commerce data, information and knowledge. The book, made up of contributions from both academia and industry, aims to present advances in artificial intelligence to collect, process, and mining Data, information and knowledge, such as new algorithms and techniques in the field, foundational theory and systems, as well as practical e-commerce applications. Some of the topics discussed include AI for e-commerce, such as machine learning, deep learning; personalized service recommendation to e-commerce; modeling, description, and verification for data, information and knowledge; and task scheduling and performance optimization for large-scale concurrency.

Table of Contents


Intelligent Processing Practices and Tools for E-Commerce Data, Information, and Knowledge

Distant Supervision for E-commerce Query Segmentation via Attention Network
The booming online e-commerce platforms demand highly accurate approaches to segment queries that carry the product requirements of consumers. Recent works have shown that the supervised methods, especially those based on deep learning, are attractive for achieving better performance on the problem of query segmentation. However, the lack of labeled data is still a big challenge for training a deep segmentation network, and the problem of out-of-vocabulary (OOV) also adversely impacts the performance of query segmentation. Different from query segmentation task in an open domain, e-commerce scenario can provide external documents that are closely related to these queries. Thus, to deal with the two challenges, we employ the idea of distant supervision and design a novel method to find contexts in external documents and extract features from these contexts. In this work, we propose a BiLSTM-CRF-based model with an attention module to encode external features, such that external context information, which can be utilized naturally and effectively to help query segmentation. Experiments on two datasets show the effectiveness of our approach compared with several kinds of baselines.
Zhao Li, Donghui Ding, Pengcheng Zou, Yu Gong, Xi Chen, Ji Zhang, Jianliang Gao, Youxi Wu, Yucong Duan
Volunteer Task Recommender in Humanitarian Supply Chain for Effective Disaster Management
An increasing trend has been observed in natural disasters entailing in significant human and infrastructural damage. This highlights the need of improvised Humanitarian Supply Chain (HSC) operations for effective response in disastrous events demanding resourceful preparation. Different challenges are faced in coordinating relief activities due to heterogeneous profiles and versatile experience of volunteers offering services for relief operation. Moreover, prioritization of HSC activities with respect to disaster damages is another concern for organizations. Lastly, while carrying out a relief operations in certain calamities, HSC task recommendation to volunteers is also a significant problem. In this paper, an optimized volunteer task recommender has been proposed based on Systems Dynamics (SD) approach that improves productivity of teams participating in relief operations. A number of parameters have been considered by recommender to assess the expertise of workforce such as: short-listing of volunteers based on evaluation of their reputation, experience level, skills, availability of volunteers, etc. The results are promising enough with optimized task recommendations to resources in effective disaster management with potential for application in real-time situations.
Farrukh Latif Butt, Sohail Sarwar, Muddesar Iqbal, Muhammad Safyan, Zia Ul Qayyum, Sattam Al Otaibi
Purpose Computation-Oriented Modeling and Transformation on DIKW Architecture
In recent years, a foreseeable AI landscape with explainable and interactive human interactions is becoming feasible based on DIKW premises. The DIKW modals are increasingly acknowledged as an important approach to address the problems related to semantic understanding beyond various question and answering systems. However, there continues to be no unified understanding over the meaning of the DIKW concepts. Data, information, knowledge, and wisdom, as a whole concept of DIKW, are also missing cohesive understanding of the relationships among them. This chapter splits the incomplete, incompletely correct, and imprecise resources into data resources D DIK, information resources I DIK, and knowledge resources K DIK according to the DIKW architecture. It narrows the complexity of resource processing and further clarifies the definition of D DIK, I DIK and K DIK. We regard information as the state after the special purpose is given to the data resource, and the specific purpose of human is the key point of data and information transformation. Therefore, we propose the new purpose resources P DIKs from the typed resources, which explain the specific purpose of human beings. Based on the existing internal relationships of these four kinds of resources, the chapter constructs the corresponding typed resource systems (DSystem, ISystem, KSystem, PSystem, and DIKPSystem) and models the systems to obtain the corresponding typed resource models (DModel, IModel, KModel, PModel, and DIKPModel). We use some examples and diagrams to clearly express and illustrate the DIKPSystem and DIKPModel and the conversion processes of D DIK, I DIK, and P DIK, which have opened up the channel of data and information transformation. Associating D DIK, I DIK, and P DIK, and combining the conversion processes and the tree systems of typed resources, can explain the abundant semantic content and reduce the storage space of resources.
Ke Fan, Yucong Duan
Toward a Blockchain-Based Rural Supply Chain Management Platform for Targeted Poverty Alleviation in China
In China, targeted poverty alleviation is a very important objective. By supporting farmers in producing their own goods, the majority of poor individuals have been lifted out of poverty. However, there are still some challenges that must be faced. This chapter presents insights into rural supply chain management development. A generalized blockchain-based supply chain management platform for targeted poverty alleviation is proposed. By taking advantage of blockchain, this platform can effectively help establish trust among participants, turn the supply chain into a trusted supply chain, and enhance the sustainability of poverty alleviation.
Rong Tan, Jing Zhang, Wen Si, Wei Zheng
Centralised Quality of Experience and Service Framework Using PROMETHEE-II for Cloud Provider Selection
The extensive diffusion of cloud services has fostered a business growth culture and innovation that propagate to many consumers and providers. For enabling a sustainable trusted relationship and for forming practicable successful service level agreements (SLAs), all stakeholders need a centralised Quality of Experience (QoE) and Quality of Service (QoS) repository that assists them in forming such an agreement. A cloud consumer needs a centralised QoE repository that supports them in selecting the right service provider that satisfies consumer’s requirements in terms of cost, reliability, efficiency and other QoS parameters. On the other end, a cloud provider needs a reliable QoS repository that provides consumers with up-to-date information about services and enables a provider to take an optimal decision to allocate the amount of marginal resources while forming an SLA. Due to the elastic nature of a cloud and lack of proper resource management, the service provider usually caught in service violation, leading to violation penalties both in terms of trust and money. Existing literature lacks studies on a centralised repository to assist cloud providers in resource management and cloud consumer service selection. To address the issue, we discuss the idea of a Centralised Quality of Experience and Service (CQoES) repository framework. The approach uses PROMETHEE-II method where each alternatives are assessed based on consumer’s custom weighted QoS attributes. The framework ensures the cloud marketplace’s economic growth and helps the interacting parties build a durable and long-term trusted relationship.
Walayat Hussain, José M. Merigó
Analysis of E-Consumer Behavior During the COVID-19 Pandemic
The issue of the evolution of consumer behavior is the subject of research since the transition from production to product marketing as consumer preferences evolve over time. The chapter presents the results of the initial phase of a study of changes in consumer behavior caused by the COVID-19 pandemic. The aim of the study is to examine specific changes in B2C interactions of Czech and Slovak consumers during the first lockdown in 2020. The starting point for changing consumer preferences was the fact that the dominant part of consumer interactions shifted from brick-and-mortar to virtual environment, where e-commerce was a safe alternative to traditional forms of trading. The results suggest that both the supply and demand sides of the market were able to adapt to the nonstandard situation in a relatively short time. From the point of view of customer behavior, an increase in B2C interactions was recorded in both monitored markets. The dominant part of the interactions shifts to the time of the standard working week, the weekend decline in interactions was significantly below the average on both sides of the market. The presented results can contribute to the formulation of qualitative assumptions for deeper empirical research in the field.
Frantisek Pollak, Peter Markovic, Jan Vachal, Roman Vavrek
Intelligent Processing Practices and Tools for E-Commerce Data, Information, and Knowledge
Honghao Gao
Prof. Jung Yoon Kim
Walayat Hussain
Dr. Muddesar Iqbal
Yucong Duan
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