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Composite Recommendation of Artworks in e-commerce Based on User Keyword-Driven Correlation Graph Search

Tsinghua Science and Technology 2024年第1期29卷 174-184页

作者： Jingyun Zhang Wenjie Zhu Byoung Jin Ahn Yongsheng Zhou School of Design Dongseo UniversityBusan 47011Republic of Korea Institute of Art and Design Jiangsu University of TechnologyChangzhou 213001China Jinling Wenyun Art Design Co. Ltd.Zhenjiang 212000China Institute of Art and Design Krirk UniversityBangkok 10220Thailand Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang 262700China

With the ever-increasing diversification of people’s interests and preferences,artwork has become one of the most popular commodities or investment goods in e-commerce,and it increasingly attracts the attention of the public.Currently,many real-world or virtual artworks can be found in e-commerce,and finding a means to recommend them to appropriate users has become a significant task to alleviate the heavy burden on artwork selection decisions by users.existing research mainly studies the problem of single-artwork recommendation while neglecting the more practical but more complex composite recommendation of artworks in e-commerce,which considerably influences the quality of experience of potential users,especially when they need to select a set of artworks instead of a single artwork.Inspired by this limitation,we put forward a novel composite recommendation approach to artworks by a user keyword-driven correlation graph search named ART_(com-rec).Through ART_(com-rec),the recommender system can output a set of artworks(e.g.,an artwork composite solution)in e-commerce by considering the keywords typed by a user to indicate his or her personalized preferences.Finally,we validate the feasibility of the ART_(com-rec) approach by a set of simulated experiments on a real-world PW dataset.

关键词： composite recommendation artwork user keywords e-commerce correlation graph search

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SMeC:Scene Mining for e-commerce

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Journal of Computer Science & Technology 2024年第1期39卷 192-210页

作者：王罡李翔郭子义殷大伟马帅 State Key Laboratory of Software Development environment Beihang UniversityBeijing 100191China School of Data Science and engineering East China Normal UniversityShanghai 200062China *** Inc.Beijing 100176China Baidu Inc.Beijing 100085China

Scene-based recommendation has proven its usefulness in e-commerce,by recommending commodities based on a given scene.However,scenes are typically unknown in advance,which necessitates scene discovery for e-commerce.In this article,we study scene discovery for e-commerce systems.We first formalize a scene as a set of commodity cate-gories that occur simultaneously and frequently in real-world situations,and model an e-commerce platform as a heteroge-neous information network(HIN),whose nodes and links represent different types of objects and different types of rela-tionships between objects,respectively.We then formulate the scene mining problem for e-commerce as an unsupervised learning problem that finds the overlapping clusters of commodity categories in the HIN.To solve the problem,we pro-pose a non-negative matrix factorization based method SMeC(Scene Mining for e-commerce),and theoretically prove its convergence.Using six real-world e-commerce datasets,we finally conduct an extensive experimental study to evaluate SMeC against 13 other methods,and show that SMeC consistently outperforms its competitors with regard to various evaluation measures.

关键词： graph clustering e-commerce heterogeneous information network(HIN) scene mining

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Research on Credit Regulation Mechanism of e-commerce Platform Based on evolutionary Game Theory

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Journal of Systems Science and Systems engineering 2024年第3期33卷 330-359页

作者： Zeguo Qiu Yuchen Yin Yao Yuan Yunhao Chen School of Computer and Information engineering Harbin University of CommerceHarbin150028China School of Management Harbin University of CommerceHarbin150028China

In the growing e-commerce industry,the problem of malicious business operations has become increasingly prominent,exposing many problems such as weak supervision of e-commerce platforms and no way for consumers to complain.In order to solve the problems of counterfeit and shoddy products on e-commerce platforms and promote the sustainable development of the e-commerce industry in China,this paper constructs a three-party evolutionary game model of e-commerce platforms,merchants,and consumers,investigates the influence of each influencing factor on each party’s strategy choice,and provides targeted suggestions to e-commerce platforms based on relevant factors.Finally,the impact of several important parameters on the equilibrium solution is discussed through sensitivity analysis.The results show that:1)the smaller the cost difference between active and negative regulation,the more the e-commerce platform tends to active regulation strategy,but increasing fines for dishonest merchants and consumer complaints have little impact on the e-commerce platform;2)increasing consumer compensation,creating an honest business environment,and reducing the cost of honest business all help companies tend to operate in good faith;3)the only factor that affects the tendency of consumer complaint strategies is the cost of complaints.The loss suffered by silence and the compensation given to consumers have little effect on consumers’tendency to complain strategy.The results can provide theoretical guidance for participants to make useful strategic decisions in the e-commerce market.

关键词： evolutionary game e-commerce data simulation

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Interregional Value-Added Tax in the era of e-commerce: Tax Policy Design Based on Big Data from Online Retailing

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Journal of Social Computing 2024年第1期5卷 46-57页

作者： Yu Chen Luwei Xiang Hu Yang School of Public Finance and Taxation Central University of Finance and EconomicsBeijing 100008China School of Management and economics The Chinese University of Hong KongShenzhen 518172China School of Information Central University of Finance and EconomicsBeijing 100008China

The value-added tax(VAT)in China is levied and allocated based on the origin principle.Under the background of the increasing substitution of online retail for traditional offline retail,this mechanism will exacerbate the disparity of regional tax revenue,and intensify tax competition among local governments.Therefore,reconsidering the allocation mechanism of value-added tax in China can be an important policy decision,and it is influenced by various economic and social factors.Firstly,we utilize large-scale retail transaction data from an e-commerce platform to measure regional disparities in retail and consumption among different regions and then reveals present tax policy results in revenue imbalance in different regions.Secondly,we establish a model based on game theory to illustrate how the origin principle leads to fierce tax competition among regions.Furthermore,by establishing and solving tax allocation models between local governments and the central government,this study simulates and calculates the degree of revenue imbalance under different scenarios and attempts to propose policy measures.The results indicate that implementing the destination principle will reduce regional tax imbalances.Moreover,adjusting the allocation ratio between the central government and local governments based on city levels is advantageous for further reducing regional tax revenue disparities.

关键词： value-added tax e-commerce destination principle origin principle

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Item-level Forecasting for e-commerce Demand with High-dimensional Data Using a Two-stage Feature Selection Algorithm

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Journal of Systems Science and Systems engineering 2022年第2期31卷 247-264页

作者： Hongyan Dai Qin Xiao Nina Yan Xun Xu Tingting Tong Business School Central University of Finance and EconomicsBeijing 100081China Department of Management Operationsand MarketingCollege of Business AdministrationCalifornia State UniversityStanislausTurlockCA95382United States Institute of Supply Chain Analytics&International Business College Dongbei University of Finance and EconomicsDalian 116025China

With the rapid development of information technology and fast growth of Internet users,e-commerce nowadays is facing complex business environment and accumulating large-volume and highdimensional data.This brings two challenges for demand forecasting.First,e-merchants need to find appropriate approaches to leverage the large amount of data and extract forecast features to capture various factors affecting the demand.Second,they need to efficiently identify the most important features to improve the forecast accuracy and better understand the key drivers for demand changes.To solve these challenges,this study conducts a multi-dimensional feature engineering by constructing five feature categories including historical demand,price,page view,reviews,and competition for e-commerce demand forecasting on item-level.We then propose a two-stage random forest-based feature selection algorithm to effectively identify the important features from the high-dimensional feature set and avoid overfittlng.We test our proposed algorithm with a large-scale dataset from the largest e-commerce platform in China.The numerical results from 21,111 items and 109 million sales observations show that our proposed random forest-based forecasting framework with a two-stage feature selection algorithm delivers 11.58%,5.81%and 3.68%forecast accuracy improvement,compared with the Autoregressive Integrated Moving Average(ARIMA),Random Forecast,and Random Forecast with one-stage feature selection approach,respectively,which are widely used in literature and industry.This study provides a useful tool for the practitioners to forecast demands and sheds lights on the B2C e-commerce operations management.

关键词： Forecasting e-commerce high-dimensional feature feature selection

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Market capitalization shock effects on open innovation models in e‑commerce:golden cut q‑rung orthopair fuzzy multicriteria decision‑making analysis

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Financial Innovation 2023年第1期9卷 1476-1500页

作者： Nikita Moiseev Alexey Mikhaylov Hasan Dinçer Serhat Yüksel Department of Mathematical Methods in economics Plekhanov Russian University of EconomicsMoscowRussia Financial University Under the Government of the Russian Federation MoscowRussia The School of Business İstanbul Medipol UniversityIstanbulTurkey

This research paper analyzes revenue trends in e-commerce,a sector with an annual sales volume of more than 340 billion dollars.The article evaluates,despite a scarcity of data,the effects on e-commerce development of the ubiquitous lockdowns and restriction measures introduced by most countries during the pandemic period.The analysis covers monthly data from January 1996 to February 2021.The research paper analyzes relative changes in the original time series through the autocorrelation function.The objects of this analysis are Amazon and Alibaba,as they are benchmarks in the e-commerce industry.This paper tests the shock effect on the e-commerce companies Alibaba in China and Amazon in the USA,concluding that it is weaker for companies with small market capitalizations.As a result,the effect on estimated e-trade volume in the USA was approximately 35%in 2020.Another evaluation considers fuzzy decision-making methodology.For this purpose,balanced scorecard-based open financial innovation models for the e-commerce industry are weighted with multistepwise weight assessment ratio analysis based on q-rung orthopair fuzzy sets and the golden cut.Within this framework,a detailed analysis of competitors should be made.The paper proves that this situation positively affects the development of successful financial innovation models for the e-commerce industry.Therefore,it may be possible to attract greater attention from e-commerce companies for these financial innovation products.

关键词： e-commerce Golden cut Open innovation Sustainable trade

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enhanced e-commerce Fraud Prediction Based on a Convolutional Neural Network Model

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Computers, Materials & Continua 2023年第4期75卷 1107-1117页

作者： Sumin Xie Ling Liu Guang Sun Bin Pan Lin Lang Peng Guo Chenzhou Vocational Technical College Chenzhou423000China Hunan University of Finance and economics Changsha410205China University Malaysia Sabah Kota Kinabalu88999Malaysia

The rapidly escalating sophistication of e-commerce fraud in recent years has led to an increasing reliance on fraud detection methods based on machine learning.However,fraud detection methods based on conventional machine learning approaches suffer from several problems,including an excessively high number of network parameters,which decreases the efficiency and increases the difficulty of training the network,while simultaneously leading to network overfitting.In addition,the sparsity of positive fraud incidents relative to the overwhelming proportion of negative incidents leads to detection failures in trained networks.The present work addresses these issues by proposing a convolutional neural network(CNN)framework for detecting ecommerce fraud,where network training is conducted using historical market transaction data.The number of network parameters reduces via the local perception field and weight sharing inherent in the CNN framework.In addition,this deep learning framework enables the use of an algorithmiclevel approach to address dataset imbalance by focusing the CNN model on minority data classes.The proposed CNN model is trained and tested using a large public e-commerce service dataset from 2018,and the test results demonstrate that the model provides higher fraud prediction accuracy than existing state-of-the-art methods.

关键词： CNN model detection e-commerce fraud

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Forecasting e-commerce Adoption Based on Bidirectional Recurrent Neural Networks

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Computers, Materials & Continua 2022年第3期70卷 5091-5106页

作者： Abdullah Ali Salamai Ather Abdulrahman Ageeli el-Sayed M.el-kenawy Community college Jazan UniversityJazanKingdom of Saudi Arabia Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt

e-commerce refers to a system that allows individuals to purchase and sell things online.The primary goal of e-commerce is to offer customers the convenience of not going to a physical store to make a purchase.They will purchase the item online and have it delivered to their home within a few days.The goal of this research was to develop machine learning algorithms that might predict e-commerce platformsales.A case study has been designed in this paper based on a proposed continuous Stochastic Fractal Search(SFS)based on a Guided Whale Optimization Algorithm(WOA)to optimize the parameter weights of the Bidirectional Recurrent Neural Networks(BRNN).Furthermore,a time series dataset is tested in the experiments of e-commerce demand forecasting.Finally,the results were compared to many versions of the state-of-the-art optimization techniques such as the Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),and Genetic Algorithm(GA).A statistical analysis has proven that the proposed algorithm can work significantly better by statistical analysis test at the P-value less than 0.05 with a one-way analysis of variance(ANOVA)test applied to confirm the performance of the proposed ensemble model.The proposed Algorithm achieved a root mean square error of RMSe(0.0000359),Mean(0.00003593)and Standard Deviation(0.000002162).

关键词： Neural networks e-commerce forecasting risk management machine learning

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enhancement of e-commerce Service by Designing Last Mile Delivery Platform

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Computer Systems Science & engineering 2022年第7期42卷 49-67页

作者： Ali Alkhalifah Fadwa Alorini Reef Alturki Department of Information Technology College of ComputerQassim UniversityBuraydah52211Saudi Arabia

The revolution of technology and the rapid evolution of the digital world had a signiﬁcant effect on the development and expansion of e-commerce.Last mile delivery,for which different app-based delivery services have recently emerged,is a new area of research that is not thoroughly addressed.Delivery ser-vice is one of the supporting platforms of e-commerce.One of the delivery issues is that many customers experience difﬁculties in communicating and coordinating with the logistics companies responsible for the delivery service.This challenge is emphasized in this study which introduces a new system to facilitate communica-tion and coordination between customers and logistics companies by using one identity and one interface.This paper is a programming-based study,as the pro-posed system evaluates a website to serve logistics companies as well as designs an application(app)to serve the customers of logistics companies.Swift,a power-ful open-source and object-oriented programming language,and the mark-up language(HTML)were used to build the last mile delivery system.In addition,Firebase,a cloud-hosted real-time database built on Google infrastructure,were used to develop the system.By increasing the level of customer satisfaction and reducing delivery failure rates,this system will eventually increase the prosperity of e-commerce.

关键词： e-commerce last mile delivery logistics companies

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Service Pricing Decision of e-commerce Supply Chain Members Considering Diseconomies of Scale and Network externalities

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Journal of Systems Science and Information 2022年第5期10卷 425-444页

作者： Yuyan WANG Ying CUI Liang SHeN T.e.C CHeNG School of Management Science and engineering Shandong University of Finance and EconomicsJinan250014China School of Public Finance and Taxation Shandong University of Finance and EconomicsJinan 250014China PolyU Business School The Hong Kong Polytechnic UniversityHung HomKowloonHong Kong 999077China

Considering diseconomies of scale and network externalities in the e-commerce supply chain(eCSC),we construct an e-platform-led benchmark model and derive the optimal decisions.Then,the model is extended by endogenizing the impact of service level on network externalities.Considering service investment that includes fixed and variable investments,the model is further extended.Comparing the extended models with the benchmark model,we found the following conclusions.Although the e-platform dominates the eCSC,its profit is lower than the manufacturer.The corporate profits,service level,and price increase with network externalities.Increases in diseconomies of scale decrease the corporate profits and service level,but increase the price.A high-quality service combined with network externalities can achieve synergy and improve the e-platform’s economies of scale,further generating a higher profit.Improving network externalities promotes the fair profit distributionin eCSC and achieves stable development.

关键词： e-commerce supply chain diseconomies of scale network externalities service level

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