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Shop Weatherly – A Weather based Smart E-Commerce System Using CNN

Jawaria Sallar, Sallar Khan, Shariq Ahmed, Parshan Kumar, Hasham Faridy, Mahaveer Rathi
2021 Revista GEINTEC  
In this paper, we proposed a novel idea by using Convolutional Neural Network Algorithm of deep learning for developing an e-commerce platform that is unique in a way that it recommends clothes according  ...  People go shopping when the weather gets changed. For travelers, there is no such E-commerce platform that can recommend clothes according to any city weather.  ...  Sallar Khan for his continuous support in the paradigm of research implementation methods.  ... 
doi:10.47059/revistageintec.v11i4.2318 fatcat:ksqyhs346zcndmlcjv5nskozy4

Towards Automated Website Classification by Deep Learning [article]

Fabrizio De Fausti, Francesco Pugliese, Diego Zardetto
2021 arXiv   pre-print
Essentially, we tackle a text classification task: an algorithm must learn to infer whether an Italian enterprise performs e-commerce from the textual content of its website.  ...  Empirical evidence shows that our proposal outperforms all the alternative Machine Learning solutions already tested in Istat for the same task.  ...  informationsensing mobile devices, search engines and software logs, sensor networks, e-commerce and stock market transactions, large-scale scientific experiments, and so on.  ... 
arXiv:1910.09991v2 fatcat:khotuyt64jei7i4betrts7gvb4

AI-Empowered Persuasive Video Generation: A Survey [article]

Chang Liu, Han Yu
2021 arXiv   pre-print
In order to produce such contents to support a large applications (e.g., e-commerce), the field of artificial intelligence (AI)-empowered persuasive video generation (AIPVG) has gained traction in recent  ...  Promotional videos are rapidly becoming a popular medium for persuading people to change their behaviours in many settings (e.g., online shopping, social enterprise initiatives).  ...  Thus, using item IDs in e-commerce platforms to label VMs might introduce such noises. 7.1.2 Noise Resistant Deep Metric Learning. For large scale VM datasets, label noises are inevitable.  ... 
arXiv:2112.09401v1 fatcat:t5bsqo6shbcoleryphaawewevy

Smart Fashion: A Review of AI Applications in the Fashion Apparel Industry [article]

Seyed Omid Mohammadi, Ahmad Kalhor
2021 arXiv   pre-print
The implementation of machine learning, computer vision, and artificial intelligence (AI) in fashion applications is opening lots of new opportunities for this industry.  ...  For each task, a time chart is provided to analyze the progress through the years.  ...  Shop, ASOS fashion e-commerce 42 Hidayati [320] Graph, Auxiliary visual words, BoVW, AP clustering Celebrities, Body shape, Style 43 Ok [447] User-based CF, Graph-based random walk, ~7% R@10 Fashion  ... 
arXiv:2111.00905v2 fatcat:6n6d62lntjfu5pxmjzgi4mpv6i

Study on the limitations of Stacking Technique for Bandwidth Improvement of Microstrip Patch Antennas

2021 American journal of science & engineering  
E-commerce has reduced human effort by reducing physical work and saving time of the seller and buyer both.The main advantage of E-commerce is that the user can browse online shops and  ...  the background study, E-commerce has to Place stress on developing a method enabling be made feasible for a large spectrum of you to shine brighter  ... 
doi:10.15864/ajse.2305 fatcat:mr7d7o3rsfe4npbo6apifsi4yy

A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources [article]

Xiao Wang and Deyu Bo and Chuan Shi and Shaohua Fan and Yanfang Ye and Philip S. Yu
2020 arXiv   pre-print
We first introduce the basic concepts of HG and discuss the unique challenges brought by the heterogeneity for HG embedding in comparison with homogeneous graph representation learning; and then we systemically  ...  Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension  ...  Usually, large-scale heterogeneous objects and interactions, such as users, items, and shops, are involved in an e-commerce platform.  ... 
arXiv:2011.14867v1 fatcat:phfoxj7qsrfshfednomeok7pau

Fashion Meets Computer Vision: A Survey [article]

Wen-Huang Cheng, Sijie Song, Chieh-Yun Chen, Shintami Chusnul Hidayati, Jiaying Liu
2021 arXiv   pre-print
Fashion, mainly conveyed by vision, has thus attracted much attention from computer vision researchers in recent years.  ...  For each task, the benchmark datasets and the evaluation protocols are summarized. Furthermore, we highlight promising directions for future research.  ...  scale, rich and diverse in content in order to collect as many as possible beauty and personal care items e-commerce websites DeepFashion2 [38] 2019 491,000 a versatile benchmark of four tasks  ... 
arXiv:2003.13988v2 fatcat:ajzvyn4ck5gqxk5ht5u3mrdmba

Product-Aware Answer Generation in E-Commerce Question-Answering [article]

Shen Gao, Zhaochun Ren, Yihong Eric Zhao, Dongyan Zhao, Dawei Yin, Rui Yan
2019 arXiv   pre-print
In e-commerce portals, generating answers for product-related questions has become a crucial task.  ...  Conducted on a large-scale real-world e-commerce dataset, our extensive experiments verify the effectiveness of each module in our proposed model.  ...  ACKNOWLEDGMENTS We would like to thank the anonymous reviewers for their constructive comments.  ... 
arXiv:1901.07696v2 fatcat:lskfjm4jtzdhzo6y2mggc7cify

Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges

Ayesha Rashid, Muhammad Shoaib Farooq, Adnan Abid, Tariq Umer, Ali Kashif Bashir, Yousaf Bin Zikria
2021 Complex & Intelligent Systems  
Search engines are a major source to infer users' past searching activities to predict their intention, facilitating the vendors and manufacturers to present their products to the user in a promising manner  ...  Similarly, six important types of data sets used for this purpose have also been discussed in this work.  ...  In [30] , an encoder-decoder neural architecture was proposed to mine users browse or purchase intents and behaviors from large scale datasets of e-commerce.  ... 
doi:10.1007/s40747-021-00342-9 fatcat:ak3y4ao2sbffjd5b3rbttidvjy

How to derive causal insights for digital commerce in china? a research commentary on computational social science methods

David C.W. Phang, Kanliang Wang, Qiuhong Wang, Robert J. Kauffman, Maurizio Naldi
2019 Electronic Commerce Research and Applications  
How to derive causal insights for digital commerce in China? A research commentary on computational social science methods. (2019).  ...  Context: 121 articles in e-commerce literature Data: Small data set from targeted literature Large-scale lit survey yielding detailed analysis and classification 33 search strings for content, resulting  ...  What can be learned for this for development of mobile agent-based e-commerce?  ... 
doi:10.1016/j.elerap.2019.100837 fatcat:segj5fu3evdqdjveyd54w7cbwq

Explainable Recommendation: A Survey and New Perspectives [article]

Yongfeng Zhang, Xu Chen
2020 arXiv   pre-print
In recent years, a large number of explainable recommendation approaches -- especially model-based methods -- have been proposed and applied in real-world systems.  ...  In this survey, we provide a comprehensive review for the explainable recommendation research.  ...  Any opinions, findings and conclusions expressed in this material are those of the authors and do not necessarily reflect those of the sponsors.  ... 
arXiv:1804.11192v10 fatcat:scsd3htz65brbiae35zd3nixe4

The Emerging Trends of Multi-Label Learning [article]

Weiwei Liu, Xiaobo Shen, Haobo Wang, Ivor W. Tsang
2020 arXiv   pre-print
Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data.  ...  Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep  ...  To accelerate it on large-scale e-commerce data, they also propose a heterogeneous graph-based variant that runs on the user-item bipartite graph directly.  ... 
arXiv:2011.11197v2 fatcat:hu6w4vgnwbcqrinrdfytmmjbjm

Fashion Recommendation Systems, Models and Methods: A Review

Samit Chakraborty, Md. Saiful Hoque, Naimur Rahman Jeem, Manik Chandra Biswas, Deepayan Bardhan, Edgar Lobaton
2021 Informatics  
On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users.  ...  This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion  ...  Acknowledgments: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  ... 
doi:10.3390/informatics8030049 fatcat:2djpad5hwraqnb6v24pg4b4a5m

Demystifying Core Ranking in Pinterest Image Search [article]

Linhong Zhu
2018 arXiv   pre-print
In this work, we share how we practically design and deploy various ranking pipelines into Pinterest image search ecosystem.  ...  Pinterest Image Search Engine helps hundreds of millions of users discover interesting content everyday. This motivates us to improve the image search quality by evolving our ranking techniques.  ...  Thanks very much to the entire teams of engineers in search feature, search quality and search infra, especially to Chao Tan, Randall Keller, Wenchang Hu, Matthew Fong, Laksh Bhasin, Ying Huang, Zheng  ... 
arXiv:1803.09799v1 fatcat:jwrua7dkcjg7dcbaddq3xziyru

Deep Learning in Information Security [article]

Stefan Thaler, Vlado Menkovski, Milan Petkovic
2018 arXiv   pre-print
Deep Learning is a sub-field of machine learning, which uses models that are composed of multiple layers.  ...  If DL-methods succeed to solve problems on a data type in one domain, they most likely will also succeed on similar data from another domain.  ...  Very Deep Convolutional Networks for Large-Scale Image Recognition. ArXiv e-prints, 2014. [137] Suphannee Sivakorn, Iasonas Polakis, and Angelos D. Keromytis.  ... 
arXiv:1809.04332v1 fatcat:xfb7lgrkw5cirdl3qvmg3ssnbi
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