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An Empirical Study of E-Commerce Website Success Model

Junjun Li, Jianjun Sun
2009 2009 International Conference on Management and Service Science  
In the Rakuten data challenge on taxonomy Classification for eCommerce -scale Product Catalogs, we propose an approach based on deep convolutional neural networks to predict product taxonomies using their  ...  The best classification accuracy is obtained through ensembling multiple networks trained differently with multiple inputs comprising of various extracted features.  ...  ACKNOWLEDGMENTS The author would like to thank the organizer of SIGIR 2018 eCom Data Challenge (Rakuten Institute of Technology Boston (RIT-Boston)) for their support.  ... 
doi:10.1109/icmss.2009.5302176 fatcat:oazunshqtzbora5syyfqxgyjee

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

Saeed Nosratabadi, Amir Mosavi, Puhong Duan, Pedram Ghamisi, Ferdinand Filip, Shahab S. Band, Uwe Reuter, Joao Gama, Amir H. Gandomi
2020 Zenodo  
The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models.  ...  Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency.  ...  Acknowledgments: Support of the Alexander von Humboldt Foundation is acknowledged. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.5281/zenodo.4087812 fatcat:4flgeabkxvgjrpbydfby3v6tua

Big Data

Weike Pan, Qiang Yang, Charu Aggarwal, Christoph Koch
2017 IEEE Intelligent Systems  
it before exploring six challenges in the context of big G u e s t e d i t o r s ' i n t r o d u c t i o n data analytics.  ...  Empirical studies on six largescale datasets from a live production environment show an improvement of 3 to 8 percent over multinomial logistic regression, support vector machines, and boosting.  ... 
doi:10.1109/mis.2017.32 fatcat:f63q5p6alnhmnl4g3wxrx3hbuu

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Saeed Nosratabadi, Amirhosein Mosavi, Puhong Duan, Pedram Ghamisi, Ferdinand Filip, Shahab S. Band, Uwe Reuter, Joao Gama, Amir H. Gandomi
2020 Mathematics  
The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models.  ...  Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency.  ...  Acknowledgments: Support of the Alexander von Humboldt Foundation is acknowledged. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/math8101799 fatcat:mcbncvat4rfxpd6ob5gkl72qiy

A Taxonomy of Data Quality Challenges in Empirical Software Engineering

Michael Franklin Bosu, Stephen G. MacDonell
2013 2013 22nd Australian Software Engineering Conference  
In this paper we propose a taxonomy of data quality challenges in empirical software engineering, based on an extensive review of prior research.  ...  Reliable empirical models such as those used in software effort estimation or defect prediction are inherently dependent on the data from which they are built.  ...  This method was demonstrated successfully using a case study of a proprietary e-commerce application drawn from the Maven2 Java library repository.  ... 
doi:10.1109/aswec.2013.21 dblp:conf/aswec/BosuM13 fatcat:aymf7lmw7rewfimqbalv35gk3i

User Response Prediction in Online Advertising [article]

Zhabiz Gharibshah, Xingquan Zhu
2021 arXiv   pre-print
We propose a taxonomy to categorize state-of-the-art user response prediction methods, primarily focus on the current progress of machine learning methods used in different online platforms.  ...  Recent years have witnessed a significant increase in the number of studies using computational approaches, including machine learning methods, for user response prediction.  ...  The study in [179] followed a cascading version of an ensemble model which includes two learners.  ... 
arXiv:2101.02342v2 fatcat:clgefamcd5fmbeg5ephizy3zqu

Comprehensive Comparative Study of Multi-Label Classification Methods [article]

Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski, Dragi Kocev
2021 arXiv   pre-print
Several studies provide reviews of methods and datasets for MLC and a few provide empirical comparisons of MLC methods. However, they are limited in the number of methods and datasets considered.  ...  This work provides a comprehensive empirical study of a wide range of MLC methods on a plethora of datasets from various domains.  ...  [20] present an empirical study of 7 different base learners used in ensembles on 20 datasets.  ... 
arXiv:2102.07113v2 fatcat:jtjefamw35fetjtnatmjvjl544

A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances

Chengyu Wang, Xiaofeng He, Aoying Zhou
2017 Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing  
A taxonomy is a semantic hierarchy, consisting of concepts linked by is-a relations.  ...  In this paper, we overview recent advances on taxonomy construction from free texts, reorganizing relevant subtasks into a complete framework.  ...  Chengyu Wang would also like to thank the ECNU Outstanding Doctoral Dissertation Cultivation Plan of Action under Grant No. YB2016040 for the support of his research.  ... 
doi:10.18653/v1/d17-1123 dblp:conf/emnlp/WangHZ17 fatcat:onmgzexqubbw7ixavokbrv2l64

Conversational Agents in Software Engineering: Survey, Taxonomy and Challenges [article]

Quim Motger, Xavier Franch, Jordi Marco
2021 arXiv   pre-print
The use of natural language interfaces in the field of human-computer interaction is undergoing intense study through dedicated scientific and industrial research.  ...  As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents' field, which is expected to help researchers and to lay the groundwork for future  ...  In the context of conversational agents, ensemble learning models like decision trees or random forests are used for input text classification through a voting approach to determine the most suitable response  ... 
arXiv:2106.10901v1 fatcat:bqs3tfkjcjhmblnd6lcysttlgy

Sustainability in software engineering: a systematic literature review

B. Penzenstadler, V. Bauer, C. Calero, X. Franch
2012 16th International Conference on Evaluation & Assessment in Software Engineering (EASE 2012)  
We sketch a taxonomy of their topics and domains, and provide lists of used methods and proposed approaches.  ...  , used methods, available studies, and considered domains.  ...  Furthermore, there are some empirical publications and rather few discussions. RQ5: Which methods are in use?  ... 
doi:10.1049/ic.2012.0004 dblp:conf/ease/PenzenstadlerBCF12 fatcat:iysbuthz7ffpfplk5aayunjole

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
2021 Information Fusion  
This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions  ...  Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification (UQ) methods.  ...  Acknowledgment This work was partially supported by the Australian Research Council's Discovery Projects funding scheme (project DP190102181) and the Natural Sciences and Engineering Research Council of  ... 
doi:10.1016/j.inffus.2021.05.008 fatcat:yschhguyxbfntftj6jv4dgywxm

A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges

Ansam Khraisat, Ammar Alazab
2021 Cybersecurity  
It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure.  ...  This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS  ...  Acknowledgments The research is supported by the Internet Commerce Security Laboratory, Federation University Australia. The authors are grateful to the Centre for.  ... 
doi:10.1186/s42400-021-00077-7 fatcat:32nrdpgvkjg4ljjxc44rewc55y

Deep Neural Networks and Tabular Data: A Survey [article]

Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, Gjergji Kasneci
2022 arXiv   pre-print
To facilitate further progress in the field, this work provides an overview of state-of-the-art deep learning methods for tabular data.  ...  Our second contribution is to provide an empirical comparison of traditional machine learning methods with eleven deep learning approaches across five popular real-world tabular data sets of different  ...  A huge stimulus was the rise of e-commerce, which demanded novel solutions, especially in advertising [15] , [69] .  ... 
arXiv:2110.01889v3 fatcat:4d4lwkzfjrb75bofncvn2x5ohi

A Review of Recommender Systems for Choosing Elective Courses

Mfowabo Maphosa, Wesley Doorsamy, Babu Paul
2020 International Journal of Advanced Computer Science and Applications  
Recommender systems have their origins in commerce and are used in other sectors such as education. Recommender systems offer an alternative to the use of human advisors.  ...  This study identified gaps in current research on the use of recommender systems for choosing elective courses.  ...  Boosting is an ensemble method in which the models are not made independently, but sequentially [12] .  ... 
doi:10.14569/ijacsa.2020.0110933 fatcat:fmkt3krswjdt3g3vaahxooavye

Job Recommender Systems: A Review [article]

Corné de Ruijt, Sandjai Bhulai
2021 arXiv   pre-print
Previous studies on JRS suggest that taking such views into account in the design of the JRS can lead to improved model performance.  ...  With respect to the type of models used in JRS, authors frequently label their method as 'hybrid'. Unfortunately, they thereby obscure what these methods entail.  ...  However, likely due to the large amount of textual data, which can ideally function as features in such regression models, we are not aware of any studies using model-based CF in job recommender systems  ... 
arXiv:2111.13576v1 fatcat:hlm2dowihjd33p55jgexueefbq
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