Figure 1: Given a few input images of a real world product, our system can generate images that not only maintain high fidelity to the original product, but also recontextualize it in novel settings beyond background changes: from showcasing it in a new perspective, adding object occlusions, to creating different and realistic lighting conditions. We present a framework for high-fidelity product image recontextualization using text-to-image diffusion models and a novel data augmentation pipeline. This pipeline leverages image-to-video diffusion, in/outpainting & negatives to create synthetic training data, addressing limitations of real-world data collection for this task. Our method improves the quality and diversity of generated images by disentangling product representations and enhancing the model's understanding of product characteristics. Evaluation on the ABO dataset and a private product dataset, using automated metrics and human assessment, demonstrates the effectiveness of our framework in generating realistic and compelling product visualizations, with implications for applications such as e-commerce and virtual product showcasing.

This paper addresses the challenges of efficiently fine-tuning large language models (LLMs) by exploring data efficiency and hyperparameter optimization. We investigate the minimum data required for effective fine-tuning and propose a novel hyperparameter optimization method that leverages early-stage model performance. Our experiments demonstrate that fine-tuning with as few as 200 samples can improve model accuracy from 70\% to 88\% in a product attribute extraction task. We identify a saturation point of approximately 6,500 samples, beyond which additional data yields diminishing returns. Our proposed bayesian hyperparameter optimization method, which evaluates models at 20\% of total training time, correlates strongly with final model performance, with 4 out of 5 top early-stage models remaining in the top 5 at completion. This approach led to a 2\% improvement in accuracy over baseline models when evaluated on an independent test set. These findings offer actionable insights for practitioners, potentially reducing computational load and dependency on extensive datasets while enhancing overall performance of fine-tuned LLMs.

E-Commerce (EC) websites provide a large amount of useful information that exceed human cognitive processing ability. In order to help customers in comparing alternatives when buying a product, previous studies designed opinion summarization systems based on customer reviews. They ignored templates' information provided by manufacturers, although these descriptive information have much product aspects or characteristics. Therefore, this paper proposes a methodology coined as SEOpinion (Summa-rization and Exploration of Opinions) which provides a summary for the product aspects and spots opinion(s) regarding them, using a combination of templates' information with the customer reviews in two main phases. First, the Hierarchical Aspect Extraction (HAE) phase creates a hierarchy of product aspects from the template. Subsequently, the Hierarchical Aspect-based Opinion Summarization (HAOS) phase enriches this hierarchy with customers' opinions; to be shown to other potential buyers. To test the feasibility of using Deep Learning-based BERT techniques with our approach, we have created a corpus by gathering information from the top five EC websites for laptops. The experimental results show that Recurrent Neural Network (RNN) achieves better results (77.4% and 82.6% in terms of F1-measure for the first and second phase) than the Convolutional Neural Network (CNN) and the Support Vector Machine (SVM) technique.

Online retail usually focuses marketing efforts on improving category pages. Optimizing these eCommerce pages, full of products of a certain category, might help attract large amounts of traffic. However, some marketers applying this on-page SEO technique overlook the importance of including all relevant information at a product page level. This is surprising if we take into account that 34% of retailers consider the pre-purchase stage to be the most important part of a customer shopping experience, which happens in the product page. This is where you find out more about the product and its details.

Some marketers eager for a new digital ad alternative are also conflicted about the rise of Amazon--a competing retailer with its own in-house brands to sell--setting up a new potential source of tension. Amazon's ad revenue is on pace to double this year, to $5.83 billion, according to eMarketer. Its ad sales are expected to jump $28.4 billion over the next five years, according to Cowen & Co.--more than the combined increases in ad revenue for all television networks globally, according to figures from media-buyer GroupM. The cumulative effect is an earthquake whose tremors will be felt by anyone selling ads, including digital publishers and TV networks. Retailers like Walmart Inc., Target Corp. and Kroger Co., which get paid by brands to place products in desirable locations within their stores, are already losing business to Amazon, ad executives say.