Supervised Transfer Learning for Product Information Question Answering

Popular e-commerce websites such as Amazon offer community question answering systems for users to pose product related questions and experienced customers may provide answers voluntarily. In this paper, we show that the large volume of existing community question answering data can be beneficial when building a system for answering questions related to product facts and specifications. Our experimental results demonstrate that the performance of a model for answering questions related to products listed in the Home Depot website can be improved by a large margin via a simple transfer learning technique from an existing large-scale Amazon community question answering dataset. Transfer learning can result in an increase of about 10% in accuracy in the experimental setting where we restrict the size of the data of the target task used for training. As an application of this work, we integrate the best performing model trained in this work into a mobile-based shopping assistant and show its usefulness.Comment: 2018 17th IEEE International Conference on Machine Learning and Application

Explainable online recommendation systems with self-identity theory and attribute learning method

In recent years, Online Shopping plays an important role in daily life and how to improve the online shopping experience with Machine Learning and Recommender System has been discussed by a group of researchers. As a sub-field of Machine Learning, Computer vision has achieved significant developments during the last decade. The computer vision techniques can help machine to view images and extract useful information from images like human beings. However, the existing online recommender system has mostly used the labelled information and ignored the large amount of useful information extracted from images. This thesis proposed that the extracted information from images through computer vision techniques can be used in the current online recommender system for the improved online shopping experience. To do this, I firstly tackled the problem of insufficient data in the real online shopping environment. I proposed a pairwise constraint random forest algorithm with associating transfer learning strategy. This new algorithm can make use of weakly supervised labelled data which is relatively easy to collect in the real online shopping environment to train the attribute classification model. Secondly, I developed an explainable recommender system with self-identity theory. This new recommender framework is built based on the weakly learning algorithm proposed above to analyse human behaviours by self-identity theory from information system research. Compared with previous recommender system, my work concentrates on different customer behaviours distinguished by self-identity and result in an improved online shopping experience. In summary, there are two major contributes for this thesis. Firstly, this thesis introduces a new weakly-supervised learning approach for semantic data classification in the online shopping environment. This new algorithm can work with noise partially labelled data to achieve better accuracy for attribute learning tasks. Secondly, by analysing the recommender system with self-identity theory, a new explainable Recommender System is proposed to improve online shopping experience. Besides, we also indicate the potential of further research in combining Computer Vision in Computer Science with online shopping experience in Information System research which can determine how Computer Vision can help to solve real world problems

Artificial Intelligence in Electronic Commerce

Compared to past years the way how the world functions today is very different. This is achieved as a result of several important improvements in the field of technology and internet. These improvements have influenced every aspect of our lives starting from the way we learn, the way we work, the way we travel, the way we shop and a lot of other activities. One of the fields that were drastically changed is the field of business and commerce. The purpose of this paper is to give information about the role and impact of artificial intelligence in electronic business. The readers of the paper will get familiar and gain solid information about the field of artificial intelligence and its implementation in electronic commerce

Leveraging Multiscale Adaptive Object Detection and Contrastive Feature Learning for Customer Behavior Analysis in Retail Settings

Multiscale adaptive object detection is a powerful computer vision technique that holds great potential for customer behavior analysis in various domains. By accurately detecting and tracking objects of interest, such as customers or products, at different scales, this approach enables detailed analysis of customer behavior. It allows businesses to track customer movements, interactions with products, and dwell times, providing valuable insights into shopping patterns and preferences. The application of multiscale adaptive object detection in customer behavior analysis offers businesses the opportunity to optimize store layouts, product placements, and marketing strategies, leading to enhanced customer experiences and improved business performance. In this paper, we introduce an innovative technique for object detection that leverages contrastive feature learning to augment the efficacy of multiscale object detection. Our methodology incorporates a contrastive loss function to extract discriminative features that exhibit resilience to scale and perspective disparities. This empowers our model to precisely detect objects across a broad range of sizes and viewpoints, even in arduous scenarios encompassing partial occlusion or low contrast against the background. Through comprehensive experiments conducted on benchmark datasets, we demonstrate that our approach surpasses state-of-the-art methodologies in terms of both accuracy and efficiency

Artificial intelligence-based conversational agents used for sustainable fashion: systematic literature review.

In the past five years, the textile industry has undergone significant transformations in response to evolving fashion trends and increased consumer garment turnover. To address the environmental impacts of fast fashion, the industry is embracing artificial intelligence (AI) and immersive technologies, particularly leveraging conversational agents as personalised guides for sustainable fashion practices. In this research paper, we conduct a systematic literature review to categorise techniques, platforms, and applications of conversational agents in promoting sustainability within the fashion industry. Additionally, the review aims to scrutinise the solutions offered, identify gaps in the existing literature, and provide insights into the effectiveness and limitations of these conversational agents. Utilising a predefined search strategy on IEEE Xplore, Google Scholar, SCOPUS, and Web of Science, 15 relevant articles were selected through a step-by-step procedure based on the guidelines of the PRISMA framework. The findings reveal a notable global interest in AI-powered conversational agents, with Italy emerging as a significant centre for research in this domain. The studies predominantly focus on consumer perceptions and intentions regarding the adoption of AI technologies, indicating a broader curiosity about how individuals incorporate such innovations into their daily lives. Moreover, a substantial proportion of the studies employs diverse methods, reflecting a comprehensive approach to understanding the functionality and performance of conversational agents in various contexts. While acknowledging the historical precedence of text-based agents, the review highlights a research gap related to embodied agents. The conclusion emphasises the need for continued exploration, particularly in understanding the broader impact of these technologies on creating sustainable and environmentally-friendly business models in the e-retail sector

Role of Internet of Things (IoT) in Retail Business and Enabling Smart Retailing Experiences

Internet of Things (IoT) is anticipated to be one of the primary megatrends up in innovation. Integrated with the current and upcoming mobility of digital gadgets, it offers ground to applications in numerous domains, including retail. The capability of sensors for setting applicable, customized, real-time, and intuitive communication with buyers and customers is considered to be a driving force of traffic and exchange, a facilitator of development along the way to elevate their purchasing experience. Simultaneously, IoT can serve to further develop relationships and foundations for more viable retail business and digital store management. Currently, digitally savvy customers expect an Omnichannel experience at each touchpoint. They need to track down the ideal data at the perfect time at the right location. Location-based innovation in a retail setting identifies the way that users take to arrive at specific areas of a retail store and helps upgrade the shopping experience. This is the reason the Internet of Things (IoT) is beginning to take the online business to a higher level, and will probably disrupt the conventional retail processes on a significant scale in the coming time. This paper surveys and arranges the most common applications of IoT and solutions for successful marketing at retail from the point of retailers and customers as well as from the point of manufacturers confronting framework or communication-related issues. We propose a model that demonstrates the potential that IoT has as compared to standard industry practices of retail to drive business results and gain an upper hand. In this paper, we’ve likewise talked about the new developments and new techniques for the organizations to accomplish competitive advantage brought about by the uses cases of IoT, particularly in the field of mobile sensors. Such developments are likely the most prominent factor in the coming years to make progress in the advanced economy

New Concepts for Efficient Consumer Response in Retail Influenced by Emerging Technologies and Innovations

The retail industry is continuously confronted with new challenges and experiences a transformation from a supplier’s market to a buyer's market. It is, thus, essential for the retail industry to consequently focus on, anticipate and fulfil consumer’s demands. Technologies and innovative business solutions can help to support to establish a required customer experience and, thereby, gain a competitive advantage. A multitude of new services and products, channels as well as players can already be identified which drive the transformation. Therefore, retailers need to understand current trends and technologies and identify as well as implement relevant solutions for their transformation since otherwise, new players will dominate the market. Hence, this dissertation aims to review and analyse new technologies which are coupled with innovative business activities in order to provide customer-centric retailing. For this purpose, this dissertation consists of five articles and derives four major contributions which introduce different approaches to establishing consumer satisfaction. Firstly, a core technology for retail is artificial intelligence (AI) which can be meaningful applied along the entire value chain and improve retailers’ positions. Two focus areas have been identified in this context which are (i) the optimisation of the entire retail value chain with the help of AI with the aim to derive transparency and (ii) the improvement of consumer satisfaction and relationship. Secondly, focussing on the consumer-retailer relationship in the digital era, a concept with a data architecture is proposed based on a real use case. The outcome was that a specific customer orientation based on data can increase the brand value and sales volume. Thirdly, the work presents that new shopping concepts, named unmanned store concepts, gain continuous growth. Unmanned store concepts employ a variety of new technologies, are characterised by attributes of speed, ease, as well as comfort, and are deemed to be the new ideal of the expectations of modern buyers. Two different directions have been deeper analysed: (i) walk-in stores and (ii) automated vending machines. The critical success factors for the usage of unmanned store solutions are distance as well as high consumer affinity for innovations. In times of the COVID-19 pandemic, which has a huge impact on retail, a continuous innovation capability still needs to be established. Finally, this work introduces a tool for systematic innovation management considering the current circumstances. Taken as a whole, this dissertation with its five articles deals with significant research questions which have not been approached so far. Thereby, the literature is extended by the introduction of novel insights and the provision of a deeper understanding of how retailers can transform their business into a more consumer-oriented way