With the increasing importance of ecommerce ( electronic commerce ) for marketing and distributing consumer goods to end users , companies will have to face new challenges . Traditional retailers and manufacturers have to develop a corporate strategy to establish their presence on the Internet . With the advance and the spread of communication devices , users are increasingly interacting with the applications on the Internet . This modern technology has been widespread and many industries are utilizing it , such as ecommerce . The literature stated that , any technology/products that are developed , it should be first accepted by its users . Now a day many firms has used Artificial intelligent ( AI ) in their ecommerce processes . Such as Apple , Amazon , Amazon and Intel use Artificial intelligent through both their services and their products , some other company using AI to help company automatic analyze customer data as well as help manager make decision . According to studies and surveys conducted world‐wide , government incentives and current legal frameworks , private initiatives and investments , technology available at a reasonable price , and public acceptance of the internet as an efficient medium for buying goods and services are driving ecommerce growth . Academics , information technology experts and even politicians in Greece are aware of these essential requirements as well . Existing systems are either unavailable in Pakistan or not suitable for local entrepreneurs . In this paper , current potentials for ecommerce growth are explored and a new protocol " Dual-Network E-Payments Protocol " is proposed . Protocol is based on combination of IP and GSM networks . The use of agent technology to support Ecommerce operations , especially in automating the buying and selling process is so promising and is worth of success . Electronic commerce grows rapidly over the Internet . This research proposed model is able to take advantage of Blockchain technology to develop e-commerce especially consumer toconsumer . The proposed model adds some advantages to ecommerce operations , and the possibility of developing them to reach a high percentage of profits by using blockchain technology which led to verify the information of products offered for sale . In addition , to the possibility of distributing feedback to all Blockchain users , through which it develops the mechanism of trust and cooperation between consumers , it is considered a reference point to explore the behaviour of commercial consumers which is stored in the data file of consumers . Small and Medium-sized Enterprises ( SMEs ) in Africa do make a lot of contribution to the national development of their respective countries . They face a number of challenges , to which a number of literatures have been well documented on , ranging from the adoption of Information and Communications Technology ( ICT ) to ecommerce security . However , little has been documented on the reasons why they are not embracing Big Data and Cloud Computing or if they have embraced it , why are they not growing as they should ? In this paper , we proposed a model which includes six standards and twenty seven elements with fuzzy data mining method to detect and assess the ecommerce phishing risk rate . Although lack of the specific data to do experiment to check the validity , it is a good start to focus on the ecommerce security . It also discusses the way , how the underlying technologies used in the implementation . Extensive use and implementation of this system can make significant contribution , in the economic development of countries with emerging ecommerce fields . Authors developed a trusted ecommerce data service as a public infrastructure . Ecommerce trading participants , commodities and their makers are to register the specified information to the data service . E-commerce applications are becoming popular day by day as they are working like a virtual shop . Today ’s distributed ecommerce applications typically rely upon various technologies in their realization , including the web , scripting languages , server-side processing and an underlying database . The combination of these technologies creates a system that requires attention to the security issues of each component and the system as a whole . However , there are lots of intrusion issues on E-Commerce . Due to this main problem , this research project team A Secure Transaction Framework for Client-Server Based ECommerce have been doing research specialized in three main components which are , website and server security , authorised data security on client-server and secure data transfer on network layer . Research on website and server security currently have been developing Web Document Integrity Detector ( WebDID ) to overcome intrusion in website and web server . On the other hand , incongruences between heterogeneous business units are likely to occur given role , socio-economic , demographic and communication characteristic differences . This article explores interdependencies between business units and its impact on a rapidly expanding ecommerce firm . In late 2009 , M.com— incidentally one of China ’s top three B2C ecommerce platforms for consumer electronics— announced a split of its Information Systems department into front and back offices , with enterprise-wide ramifications . Similarity search is a core component in various applications such as image matching and product recommendation . However , single-machine solutions are usually insufficient due to the large cardinality of modern datasets . This paper proposes a co-operation framework for multiple role-based case-based reasoning ( CBR ) agents to handle the product recommendation problem for e-commerce applications . Each agent has different case structure with intersecting features and agents exploit all information related to the problem by co-operation , which is accomplished through the merge of distributed cases in order to form cases having better representation of the problem . An image recommendation method , comprising : ( 1 ) generating a user profile by building a model every predetermined time from a customer purchase list database ; And ( 2 ) recommending a multimedia image having a high probability of being purchased by the target user using the constructed model . According to the user profile-based image recommendation method and apparatus of the present invention , by using a user profile-based image recommendation method that combines content-based filtering and collaborative filtering to solve a new product recommendation problem , it is recommended when the input data for customer preferences is sparse . It is possible to provide an image recommendation method that solves the problem of inaccurate results and the inability to recommend a new product . Abstract Large sparse datasets are very common in product recommendation applications . For the scalability reason , linear classifiers are preferred in classification tasks on such datasets . The Priori algorithm is a logic calculation which combines a particular product with other products through transaction history data . Based on the results of testing the Apriori Algorithm on Product Recommendation Applications , which were carried out in 5 stages in a priori the results showed that the Priori Algorithm runs according to the scenario generated from manual calculations . Thus it can be said that the application made is able to help Al-Veera stores in promoting their products Nevertheless , multiattribute recommender systems are usually more sophisticated than single-attribute ones , and their implementation may prove complex to e-market system developers . This paper presents the design , development and evaluation of marService , a product recommendation service that is based on Multi-Attribute Utility Theory ( MAUT ) . This approach studies the application of marService for providing wine recommendations in an existing e-market and presents the results of a simulation experiment . Knowledge-based recommendation has proven to be useful approach for product recommendation where individual products are described in terms of well defined set of features . However existing knowledge-based recommendation systems lack on some issues : it does not allow customers to set importance to the feature with matching options and , it is not possible to set cutoff at individual feature as well as case level at runtime during product recommendation process . In this paper , we have presented a hybrid approach , which integrates rule based reasoning ( RBR ) and case based reasoning ( CBR ) techniques to address these issues . Recommending appropriate product items to the target user is becoming the key to ensure continuous success of E-commerce . Today , many E-commerce systems adopt various recommendation techniques , e.g. , Collaborative Filtering ( abbreviated as CF)-based technique , to realize product item recommendation . Overall , the present CF recommendation can perform very well , if the target user owns similar friends ( user-based CF ) , or the product items purchased and preferred by target user own one or more similar product items ( item-based CF ) . In the method , analysis on user social relationship is introduced , and personalized product recommendation is provided for the target user based on the production type information . The calculation is simple and quick , and the method has better expandability and adaptability , so that the method is suitable for highly dynamic and immense amount of product-oriented recommendation for the internet users . ESD related failures come from two primary sources . The first are failures produced by the ESD testers as a result of product classification . Part of a new product ’s qualification process includes determining its ESD threshold and in turn , its ESD classification . Within the context of a selected knitwear company , identifies a three‐stage procedure with attention focused on the first stage . product classification . A three‐division model was developed and proved more satisfactory , when tested , than a five‐division one . Product information consists of product attributes and the relationships between products . Product categorization ( or classification ) is one type of such relationships . Ontology can play an important role in the formalization of product information . Abstract This paper aims to study the role product category plays as a moderating factor in online reviews , by introducing a novel method for product category classification using natural language processing ( NLP ) . The study includes a wide variety of categories , based on a high number of products and number of reviews . Product category is found to play an important role as a moderating factor of several properties concerning user generated online reviews - including its impact on sales . The authors introduce a novel method for product category classification using natural language processing ( NLP ) , and by applying this method show that reviews are more influential for subjectively evaluated products than objectively evaluated products . In addition , with a selection of hit products and random products for each category , it demonstrates that product popularity has a moderating impact on the effect of online reviews on sales , and that reviews tend to be more influential for the less popular products . Determining to settle such obstacles , a personalised recommendation expert system for selecting optimised skin-health product within the category based upon genetic phenotypes for each consumer was introduced in this paper . Random Forests were implemented to achieve automatic product categorisation , the performance discussed and compared with SVM and Logistic Regression . Lastly , categorised skin-health product suggestion was made with an optimised recommendation model based on associated genetic phenotype information . We propose a new approach for constructing taxonomies . Our idea is based on the proven , increased human involvement and desire to annotate web content ( e.g. , in social media and product categorization applications ) . We define the required input from humans in the form of explicit structural , e.g. , supertype-subtype relationships between concepts . Social network users , and people in general , tend to prioritize recommendations coming from people they are acquainted to . This research proposes a trust model that will estimate a trust value for content creators on an online rating system with social network capabilities . This research introduces the concept of social distance , which is drawn from clustering methods applied to the social network user base , and incorporates said distance in the estimation of trust , as well as user generated ratings . Students also found the software easy and convenient to use . We identify pedagogical and developmental issues with implementing online peer review , and outline anticipated future changes to the software . — the editorial board , staff , referees , and authors . On May 15 , 2009 , the journal implemented a ScholarOne Manuscripts ( formerly known as Manuscript Central ) system to facilitate online peer review and manuscript tracking of submitted manuscripts . To learn more , visit ITE ’s ScholorOne Manuscript system at http://mc . By using the Internet , data can be collected much faster than in an offline experiment and the administration effort is very low . The goal of our study was to find out , if there is a difference in accuracy between an online and an offline rating task of human and robot emotional facial expressions . Results indicate , that emotional expressions are best recognized in humans ( versus robots ) and in the offline ( versus online ) condition . The aggregation of k-ary preferences is a novel ranking problem that plays an important role in several aspects of daily life , such as ordinal peer grading , online image-rating , meta-search and online product recommendation . Meanwhile , crowdsourcing is increasingly emerging as a way to provide a plethora of k-ary preferences for these types of ranking problems , due to the convenience of the platforms and the lower costs . Our initial findings show some interesting insights about different personal interpretation and utilization of ratings and reviews in different contexts . We conclude by listing recommendations for designers of online rating and review systems . in an online review and rating portal , summarization of the text review is important as it provides a detailed glimpse of what is working and what is not . Usual techniques of text summarization do not work well in this domain due to unstructured nature of such reviews . Online ranker evaluation is a key challenge in information retrieval . An important task in the online evaluation of rankers is using implicit user feedback for inferring preferences between rankers . Interleaving methods have been found to be efficient and sensitive , i.e. they can quickly detect even small differences in quality . A commercial search system may evaluate a large number of rankers concurrently , and scaling effectively in the presence of numerous rankers has not been fully studied . In this paper , we focus on solving the large-scale online ranker evaluation problem under the so-called Condorcet assumption , where there exists an optimal ranker that is preferred to all other rankers . We propose Merge Double Thompson Sampling ( MergeDTS ) , which first utilizes a divide-and-conquer strategy that localizes the comparisons carried out by the algorithm to small batches of rankers , and then employs the Thompson Sampling ( TS ) to reduce the comparisons between suboptimal rankers inside these small batches .
With the increasing importance of ecommerce ( electronic commerce ) for marketing and distributing consumer goods to end users , companies will have to face new challenges . Traditional retailers and manufacturers have to develop a corporate strategy to establish their presence on the Internet .
With the advance and the spread of communication devices , users are increasingly interacting with the applications on the Internet . This modern technology has been widespread and many industries are utilizing it , such as ecommerce . The literature stated that , any technology/products that are developed , it should be first accepted by its users .
Now a day many firms has used Artificial intelligent ( AI ) in their ecommerce processes . Such as Apple , Amazon , Amazon and Intel use Artificial intelligent through both their services and their products , some other company using AI to help company automatic analyze customer data as well as help manager make decision .
According to studies and surveys conducted world‐wide , government incentives and current legal frameworks , private initiatives and investments , technology available at a reasonable price , and public acceptance of the internet as an efficient medium for buying goods and services are driving ecommerce growth . Academics , information technology experts and even politicians in Greece are aware of these essential requirements as well .
Existing systems are either unavailable in Pakistan or not suitable for local entrepreneurs . In this paper , current potentials for ecommerce growth are explored and a new protocol " Dual-Network E-Payments Protocol " is proposed . Protocol is based on combination of IP and GSM networks .
The use of agent technology to support Ecommerce operations , especially in automating the buying and selling process is so promising and is worth of success . Electronic commerce grows rapidly over the Internet .
This research proposed model is able to take advantage of Blockchain technology to develop e-commerce especially consumer toconsumer . The proposed model adds some advantages to ecommerce operations , and the possibility of developing them to reach a high percentage of profits by using blockchain technology which led to verify the information of products offered for sale . In addition , to the possibility of distributing feedback to all Blockchain users , through which it develops the mechanism of trust and cooperation between consumers , it is considered a reference point to explore the behaviour of commercial consumers which is stored in the data file of consumers .
Small and Medium-sized Enterprises ( SMEs ) in Africa do make a lot of contribution to the national development of their respective countries . They face a number of challenges , to which a number of literatures have been well documented on , ranging from the adoption of Information and Communications Technology ( ICT ) to ecommerce security . However , little has been documented on the reasons why they are not embracing Big Data and Cloud Computing or if they have embraced it , why are they not growing as they should ?
In this paper , we proposed a model which includes six standards and twenty seven elements with fuzzy data mining method to detect and assess the ecommerce phishing risk rate . Although lack of the specific data to do experiment to check the validity , it is a good start to focus on the ecommerce security .
It also discusses the way , how the underlying technologies used in the implementation . Extensive use and implementation of this system can make significant contribution , in the economic development of countries with emerging ecommerce fields .
Authors developed a trusted ecommerce data service as a public infrastructure . Ecommerce trading participants , commodities and their makers are to register the specified information to the data service .
E-commerce applications are becoming popular day by day as they are working like a virtual shop . Today ’s distributed ecommerce applications typically rely upon various technologies in their realization , including the web , scripting languages , server-side processing and an underlying database . The combination of these technologies creates a system that requires attention to the security issues of each component and the system as a whole .
However , there are lots of intrusion issues on E-Commerce . Due to this main problem , this research project team A Secure Transaction Framework for Client-Server Based ECommerce have been doing research specialized in three main components which are , website and server security , authorised data security on client-server and secure data transfer on network layer . Research on website and server security currently have been developing Web Document Integrity Detector ( WebDID ) to overcome intrusion in website and web server .
On the other hand , incongruences between heterogeneous business units are likely to occur given role , socio-economic , demographic and communication characteristic differences . This article explores interdependencies between business units and its impact on a rapidly expanding ecommerce firm . In late 2009 , M.com— incidentally one of China ’s top three B2C ecommerce platforms for consumer electronics— announced a split of its Information Systems department into front and back offices , with enterprise-wide ramifications .
Similarity search is a core component in various applications such as image matching and product recommendation . However , single-machine solutions are usually insufficient due to the large cardinality of modern datasets .
This paper proposes a co-operation framework for multiple role-based case-based reasoning ( CBR ) agents to handle the product recommendation problem for e-commerce applications . Each agent has different case structure with intersecting features and agents exploit all information related to the problem by co-operation , which is accomplished through the merge of distributed cases in order to form cases having better representation of the problem .
An image recommendation method , comprising : ( 1 ) generating a user profile by building a model every predetermined time from a customer purchase list database ; And ( 2 ) recommending a multimedia image having a high probability of being purchased by the target user using the constructed model . According to the user profile-based image recommendation method and apparatus of the present invention , by using a user profile-based image recommendation method that combines content-based filtering and collaborative filtering to solve a new product recommendation problem , it is recommended when the input data for customer preferences is sparse . It is possible to provide an image recommendation method that solves the problem of inaccurate results and the inability to recommend a new product .
Abstract Large sparse datasets are very common in product recommendation applications . For the scalability reason , linear classifiers are preferred in classification tasks on such datasets .
The Priori algorithm is a logic calculation which combines a particular product with other products through transaction history data . Based on the results of testing the Apriori Algorithm on Product Recommendation Applications , which were carried out in 5 stages in a priori the results showed that the Priori Algorithm runs according to the scenario generated from manual calculations . Thus it can be said that the application made is able to help Al-Veera stores in promoting their products
Nevertheless , multiattribute recommender systems are usually more sophisticated than single-attribute ones , and their implementation may prove complex to e-market system developers . This paper presents the design , development and evaluation of marService , a product recommendation service that is based on Multi-Attribute Utility Theory ( MAUT ) . This approach studies the application of marService for providing wine recommendations in an existing e-market and presents the results of a simulation experiment .
Knowledge-based recommendation has proven to be useful approach for product recommendation where individual products are described in terms of well defined set of features . However existing knowledge-based recommendation systems lack on some issues : it does not allow customers to set importance to the feature with matching options and , it is not possible to set cutoff at individual feature as well as case level at runtime during product recommendation process . In this paper , we have presented a hybrid approach , which integrates rule based reasoning ( RBR ) and case based reasoning ( CBR ) techniques to address these issues .
Recommending appropriate product items to the target user is becoming the key to ensure continuous success of E-commerce . Today , many E-commerce systems adopt various recommendation techniques , e.g. , Collaborative Filtering ( abbreviated as CF)-based technique , to realize product item recommendation . Overall , the present CF recommendation can perform very well , if the target user owns similar friends ( user-based CF ) , or the product items purchased and preferred by target user own one or more similar product items ( item-based CF ) .
In the method , analysis on user social relationship is introduced , and personalized product recommendation is provided for the target user based on the production type information . The calculation is simple and quick , and the method has better expandability and adaptability , so that the method is suitable for highly dynamic and immense amount of product-oriented recommendation for the internet users .
ESD related failures come from two primary sources . The first are failures produced by the ESD testers as a result of product classification . Part of a new product ’s qualification process includes determining its ESD threshold and in turn , its ESD classification .
Within the context of a selected knitwear company , identifies a three‐stage procedure with attention focused on the first stage . product classification . A three‐division model was developed and proved more satisfactory , when tested , than a five‐division one .
Product information consists of product attributes and the relationships between products . Product categorization ( or classification ) is one type of such relationships . Ontology can play an important role in the formalization of product information .
Abstract This paper aims to study the role product category plays as a moderating factor in online reviews , by introducing a novel method for product category classification using natural language processing ( NLP ) . The study includes a wide variety of categories , based on a high number of products and number of reviews .
Product category is found to play an important role as a moderating factor of several properties concerning user generated online reviews - including its impact on sales . The authors introduce a novel method for product category classification using natural language processing ( NLP ) , and by applying this method show that reviews are more influential for subjectively evaluated products than objectively evaluated products . In addition , with a selection of hit products and random products for each category , it demonstrates that product popularity has a moderating impact on the effect of online reviews on sales , and that reviews tend to be more influential for the less popular products .
Determining to settle such obstacles , a personalised recommendation expert system for selecting optimised skin-health product within the category based upon genetic phenotypes for each consumer was introduced in this paper . Random Forests were implemented to achieve automatic product categorisation , the performance discussed and compared with SVM and Logistic Regression . Lastly , categorised skin-health product suggestion was made with an optimised recommendation model based on associated genetic phenotype information .
We propose a new approach for constructing taxonomies . Our idea is based on the proven , increased human involvement and desire to annotate web content ( e.g. , in social media and product categorization applications ) . We define the required input from humans in the form of explicit structural , e.g. , supertype-subtype relationships between concepts .
Social network users , and people in general , tend to prioritize recommendations coming from people they are acquainted to . This research proposes a trust model that will estimate a trust value for content creators on an online rating system with social network capabilities . This research introduces the concept of social distance , which is drawn from clustering methods applied to the social network user base , and incorporates said distance in the estimation of trust , as well as user generated ratings .
Students also found the software easy and convenient to use . We identify pedagogical and developmental issues with implementing online peer review , and outline anticipated future changes to the software .
— the editorial board , staff , referees , and authors . On May 15 , 2009 , the journal implemented a ScholarOne Manuscripts ( formerly known as Manuscript Central ) system to facilitate online peer review and manuscript tracking of submitted manuscripts . To learn more , visit ITE ’s ScholorOne Manuscript system at http://mc .
By using the Internet , data can be collected much faster than in an offline experiment and the administration effort is very low . The goal of our study was to find out , if there is a difference in accuracy between an online and an offline rating task of human and robot emotional facial expressions . Results indicate , that emotional expressions are best recognized in humans ( versus robots ) and in the offline ( versus online ) condition .
The aggregation of k-ary preferences is a novel ranking problem that plays an important role in several aspects of daily life , such as ordinal peer grading , online image-rating , meta-search and online product recommendation . Meanwhile , crowdsourcing is increasingly emerging as a way to provide a plethora of k-ary preferences for these types of ranking problems , due to the convenience of the platforms and the lower costs .
Our initial findings show some interesting insights about different personal interpretation and utilization of ratings and reviews in different contexts . We conclude by listing recommendations for designers of online rating and review systems .
in an online review and rating portal , summarization of the text review is important as it provides a detailed glimpse of what is working and what is not . Usual techniques of text summarization do not work well in this domain due to unstructured nature of such reviews .
Online ranker evaluation is a key challenge in information retrieval . An important task in the online evaluation of rankers is using implicit user feedback for inferring preferences between rankers . Interleaving methods have been found to be efficient and sensitive , i.e. they can quickly detect even small differences in quality .
A commercial search system may evaluate a large number of rankers concurrently , and scaling effectively in the presence of numerous rankers has not been fully studied . In this paper , we focus on solving the large-scale online ranker evaluation problem under the so-called Condorcet assumption , where there exists an optimal ranker that is preferred to all other rankers . We propose Merge Double Thompson Sampling ( MergeDTS ) , which first utilizes a divide-and-conquer strategy that localizes the comparisons carried out by the algorithm to small batches of rankers , and then employs the Thompson Sampling ( TS ) to reduce the comparisons between suboptimal rankers inside these small batches .
Transaction- and Trust-Based Strategies in E-commerce - a Conceptual Approach Utilizing WAMMI Components to Evaluate the Usability of E-commerce Website The analysis of model for electronic commerce - artificial intelligent Evaluating internet usage and ecommerce growth in Greece E-commerce in Pakistan: Growth Potentials and E-payment Solutions Multi-Agent Automated Intelligent Shopping System (MAISS) Developing the E-Commerce Model A Consumer to Consumer Using Blockchain Network Technique Will Big Data and Cloud Computing Adoption by SMEs in Developing African Countries Survive? - Case Study of Ghana An Anti-phishing Model for Ecommerce under a Network Environment E Commerce Web Platform Development Trustworthiness Evaluation of Registered Information in a Trusted E-Commerce Data Service Application-Level and Database Security for E-Commerce Application Rangka kerja keselamatan transaksi bagi pelanggan pelayan berasaskan perdagangan elektronik An Examination of Interdependencies in a B2C Platform in China: The Case of M.com Pyramid: A General Framework for Distributed Similarity Search on Large-scale Datasets Co-operation framework of case-based reasoning agents for automated product recommendation The method and apparatus for image recommendation based on user profile using feature based collaborative filtering to resolve new item recommendation The convergence of linear classifiers on large sparse data Application of Apriori Algorithms on Online Shop Product Recommendation Systems (Case Study: Al-Veera Jember Muslim Clothing Store) marService: multiattribute utility recommendation for e-markets A Hybrid Approach for Knowledge-Based Product Recommendation Structural Balance Theory-Based E-Commerce Recommendation over Big Rating Data Internet information product recommending method based on matrix decomposition Failure Analysis Techniques Activity‐based Costing in Non‐standard Route Manufacturing Building an operational product ontology system Online consumers reviews: Examining the moderating effects of product type and product popularity on the review impact on sales Online Consumer Reviews : The Moderating Effect of Product Category A DNA-Based Intelligent Expert System for Personalised Skin-Health Recommendations. Δυναμική κατασκευή μεγάλης κλίμακας ταξονομίας σε Crowdsourcing περιβάλλοντα How Can Social Networks Help Us Measure Trust Online? PRAZE: Innovating teaching through online peer review From the Desk of the Editor-in-Chief Web-based vs. controled environment: About the reliability of stimuli ratings in human-robot interaction Stagewise learning for noisy k-ary preferences A study of reviews and ratings on the internet Review tree: An unsupervised method to autogenerate visual summary of online reviews An Improved Multileaving Algorithm for Online Ranker Evaluation Merge Double Thompson Sampling for Large Scale Online Ranker Evaluation
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FIT ICYCS ICEIS (1) ICEBE ICIS ICISTM ITNG RO-MAN CHI Extended Abstracts SIGIR
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