Optimizing E-Commerce Product Classification Using Transfer Learning

The global e-commerce market is snowballing at a rate of 23% per year. In 2017, retail e-commerce users were 1.66 billion and sales worldwide amounted to 2.3 trillion US dollars, and e-retail revenues are projected to grow to 4.88 trillion USD in 2021. With the immense popularity that e-commerce has gained over past few years comes the responsibility to deliver relevant results to provide rich user experience. In order to do this, it is essential that the products on the ecommerce website be organized correctly into their respective categories. Misclassification of products leads to irrelevant results for users which not just reflects badly on the website, it could also lead to lost customers. With ecommerce sites nowadays providing their portal as a platform for third party merchants to sell their products as well, maintaining a consistency in product categorization becomes difficult. Therefore, automating this process could be of great utilization. This task of automation done on the basis of text could lead to discrepancies since the website itself, its various merchants, and users, all could use different terminologies for a product and its category. Thus, using images becomes a plausible solution for this problem. Dealing with images can best be done using deep learning in the form of convolutional neural networks. This is a computationally expensive task, and in order to keep the accuracy of a traditional convolutional neural network while reducing the hours it takes for the model to train, this project aims at using a technique called transfer learning. Transfer learning refers to sharing the knowledge gained from one task for another where new model does not need to be trained from scratch in order to reduce the time it takes for training. This project aims at using various product images belonging to five categories from an ecommerce platform and developing an algorithm that can accurately classify products in their respective categories while taking as less time as possible. The goal is to first test the performance of transfer learning against traditional convolutional networks. Then the next step is to apply transfer learning to the downloaded dataset and assess its performance on the accuracy and time taken to classify test data that the model has never seen before

Building trustworthy e-Commerce wesite

The process of building consumer trust in E-Commerce is based on the presence of trust features or trust attributes in the websites. Consumer may examine e-Commerce websites for the existence of trust attributes. However, to date, which trust attributes contribute to the website’s trustworthiness and which trust attributes give more value to consumer has not been adequately explored. Therefore, the purpose of the paper is to look for the relevant trust attributes for e-Commerce websites and to identify the importance ranking of trust attributes that contribute significantly to the trustworthiness of e-Commerce website. Various journal papers and articles related to e-Commerce field have been referred in order to identify the trust attributes. An online survey that received 1230 respondents was carried out to investigate the importance ranking of ten trust attributes. The paper contributes to the discussion on how to build trust in e-Commerc

Critical Success Factors for Positive User Experience in Hotel Websites: Applying Herzberg's Two Factor Theory for User Experience Modeling

This research presents the development of a critical success factor matrix for increasing positive user experience of hotel websites based upon user ratings. Firstly, a number of critical success factors for web usability have been identified through the initial literature review. Secondly, hotel websites were surveyed in terms of critical success factors identified through the literature review. Thirdly, Herzberg's motivation theory has been applied to the user rating and the critical success factors were categorized into two areas. Finally, the critical success factor matrix has been developed using the two main sets of data.Comment: Journal articl

Cross-Domain Image Retrieval with Attention Modeling

With the proliferation of e-commerce websites and the ubiquitousness of smart phones, cross-domain image retrieval using images taken by smart phones as queries to search products on e-commerce websites is emerging as a popular application. One challenge of this task is to locate the attention of both the query and database images. In particular, database images, e.g. of fashion products, on e-commerce websites are typically displayed with other accessories, and the images taken by users contain noisy background and large variations in orientation and lighting. Consequently, their attention is difficult to locate. In this paper, we exploit the rich tag information available on the e-commerce websites to locate the attention of database images. For query images, we use each candidate image in the database as the context to locate the query attention. Novel deep convolutional neural network architectures, namely TagYNet and CtxYNet, are proposed to learn the attention weights and then extract effective representations of the images. Experimental results on public datasets confirm that our approaches have significant improvement over the existing methods in terms of the retrieval accuracy and efficiency.Comment: 8 pages with an extra reference pag

Referencial para a caracterização de websites de hotéis de acordo com as necessidades dos consumidores

Online presence is essential for tourism organisations, and the quality of websites can influence customers. In the case of hotels, there are many studies to evaluate website performance based on functionality, usability and other factors, much less on the amount of different information available to the consumer. In the near future by using Big Data it is expected that hotel websites will be dynamic, they will adapt themselves on-the-fly, showing personalized information to each consumer. Different consumers will have different websites (information? available) from the same hotel. This paper presents a framework for the characterisation of hotel websites, focusing on the amount of information available to the consumer in each website, which was applied in a case study during the last months of 2013 to the websites of five-star hotels that operate in the tourist region of the Algarve, Portugal. The framework allowed to identify a set of exhaustive indicators for hotel website characterisation, which were then grouped into ten fundamental information dimensions. These dimensions further fell into four dimension groups. Finally, it is presented and discussed quantitative and qualitative evaluations, that illustrates which indicators and dimensions are more often considered on hotel websites to satisfy the consumer?s information needs