Dark patterns are deceptive user interfaces employed by e-commerce websites to manipulate user's behavior in a way that benefits the website, often unethically. This study investigates the detection of such dark patterns. Existing solutions include UIGuard, which uses computer vision and natural language processing, and approaches that categorize dark patterns based on detectability or utilize machine learning models trained on datasets. We propose combining web scraping techniques with fine-tuned BERT language models and generative capabilities to identify dark patterns, including outliers. The approach scrapes textual content, feeds it into the BERT model for detection, and leverages BERT's bidirectional analysis and generation abilities. The study builds upon research on automatically detecting and explaining dark patterns, aiming to raise awareness and protect consumers.

Vision and vision-language applications of neural networks, such as image classification and captioning, rely on large-scale annotated datasets that require non-trivial data-collecting processes. This time-consuming endeavor hinders the emergence of large-scale datasets, limiting researchers and practitioners to a small number of choices. Therefore, we seek more efficient ways to collect and annotate images. Previous initiatives have gathered captions from HTML alt-texts and crawled social media postings, but these data sources suffer from noise, sparsity, or subjectivity. For this reason, we turn to commercial shopping websites whose data meet three criteria: cleanliness, informativeness, and fluency. We introduce the Let's Go Shopping (LGS) dataset, a large-scale public dataset with 15 million image-caption pairs from publicly available e-commerce websites. When compared with existing general-domain datasets, the LGS images focus on the foreground object and have less complex backgrounds. Our experiments on LGS show that the classifiers trained on existing benchmark datasets do not readily generalize to e-commerce data, while specific self-supervised visual feature extractors can better generalize. Furthermore, LGS's high-quality e-commerce-focused images and bimodal nature make it advantageous for vision-language bi-modal tasks: LGS enables image-captioning models to generate richer captions and helps text-to-image generation models achieve e-commerce style transfer.

Offer personalized customer service and support through a chatbot: By using a chatbot to handle customer inquiries, you can free up time for your human customer service reps to focus on more complex issues. This can lead to improved customer satisfaction and increased revenue for your business. Create and sell chatbot templates for other businesses to use: If you have experience building chatbots, you can create templates and sell them to other businesses that are looking to implement chatbots but don't have the time or expertise to build them from scratch. Use GPT technology to generate unique content for websites and blogs: GPT can be used to generate unique, high-quality content for websites and blogs. This can save time and effort for content creators, while also providing a valuable service to businesses looking for fresh, engaging content.

Do you think that e-commerce has come of age? Just think about it – you can browse thousands of dresses online to find that perfect one in just a matter of clicks. Rather than visiting physical stores, modern-day consumers prefer going online where they get access to myriads of products or services, making the shopping experience faster and better. According to Statista, it is expected to have over 2.14 billion digital buyers in the year 2021, which is a huge number for online businesses. If you are an entrepreneur, it is a great time to leverage your e-commerce brand by implementing some innovative solutions and technologies.

According to the main international reports, more pervasive industrial and business-process automation, thanks to machine learning and advanced analytic tools, will unlock more than 14 trillion USD worldwide annually by 2030. In the specific case of pricing problems-which constitute the class of problems we investigate in this paper-, the estimated unlocked value will be about 0.5 trillion USD per year. In particular, this paper focuses on pricing in e-commerce when the objective function is profit maximization and only transaction data are available. This setting is one of the most common in real-world applications. Our work aims to find a pricing strategy that allows defining optimal prices at different volume thresholds to serve different classes of users. Furthermore, we face the major challenge, common in real-world settings, of dealing with limited data available. We design a two-phase online learning algorithm, namely PVD-B, capable of exploiting the data incrementally in an online fashion. The algorithm first estimates the demand curve and retrieves the optimal average price, and subsequently it offers discounts to differentiate the prices for each volume threshold. We ran a real-world 4-month-long A/B testing experiment in collaboration with an Italian e-commerce company, in which our algorithm PVD-B-corresponding to A configuration-has been compared with human pricing specialists-corresponding to B configuration. At the end of the experiment, our algorithm produced a total turnover of about 300 KEuros, outperforming the B configuration performance by about 55%. The Italian company we collaborated with decided to adopt our algorithm for more than 1,200 products since January 2022.

SEO testing expert Emily Potter joins us once again to wrap up this season of Whiteboard Friday! Today, she takes you through a few tests that generated unexpected results for her team at SearchPilot, and what those results mean for SEO strategy. Enjoy, and stay tuned for the next season of Whiteboard Friday episodes, expected later this summer! Click on the whiteboard image above to open a high resolution version in a new tab! If you haven't heard of us before, we're an SEO A/B testing platform.

E-commerce business platforms are completely leveraging the power and analytics of AI app ideas. AI-based e-commerce applications are a compulsion today to grow business reach and online presence. AI & ML is giving an all-new real-time online shopping exposure. E-commerce websites are leveraging the power of artificial intelligence to build a competitive e-commerce website to boost sales and user experience. AI tools and features make the e-commerce website more sophisticated, easy to navigate and offer a real-time shopping experience.

This is very important for a business to have an e-commerce platform to compete with the giant -- Merchandise of online vendors. In the competitive marketplace, if your business is not able to make a remarkable appearance online. You may get left behind. A transformed and revolutionized face of technology has brought us into the digital era where we are more into gadgets, social media, and the internet like never before. Before the pandemic, life was going smoothly.

By leveraging large amounts of product data collected across hundreds of live e-commerce websites, we construct 1000 unique classification tasks that share similarly-structured input data, comprised of both text and images. These classification tasks focus on learning the product hierarchy of different e-commerce websites, causing many of them to be correlated. Adopting a multi-modal transformer model, we solve these tasks in unison using multi-task learning (MTL). Extensive experiments are presented over an initial 100-task dataset to reveal best practices for "large-scale MTL" (i.e., MTL with 100 tasks). From these experiments, a final, unified methodology is derived, which is composed of both best practices and new proposals such as DyPa, a simple heuristic for automatically allocating task-specific parameters to tasks that could benefit from extra capacity. Using our large-scale MTL methodology, we successfully train a single model across all 1000 tasks in our dataset while using minimal task specific parameters, thereby showing that it is possible to extend several orders of magnitude beyond current efforts in Figure 1: A depiction of the BERT-style, multi-modal classification MTL.

Last year, IDC predicted AI technology spending would exceed $50 billion by 2021. Cut to the present, more companies have started investing money on AI. From slow adopters like manufacturing to the healthcare industry to small and medium businesses that were unlikely to use AI earlier, there has been an uptick in usage across the board. AI is helping companies manage back-office work, administrative duties, and in streamlining the supply chain. It enables companies to maximize the productivity of employees, accelerate the speed of production, and increase their ROI.