Online marketplaces execute large volume of price updates that are initiated by individual marketplace sellers each day on the platform. This price democratization comes with increasing challenges with data quality. Lack of centralized guardrails that are available for a traditional online retailer causes a higher likelihood for inaccurate prices to get published on the website, leading to poor customer experience and potential for revenue loss. We present MoatPlus (Masked Optimal Anchors using Trees, Proximity-based Labeling and Unsupervised Statistical-features), a scalable price anomaly detection framework for a growing marketplace platform. The goal is to leverage proximity and historical price trends from unsupervised statistical features to generate an upper price bound. We build an ensemble of models to detect irregularities in price-based features, exclude irregular features and use optimized weighting scheme to build a reliable price bound in real-time pricing pipeline. We observed that our approach improves precise anchor coverage by up to 46.6% in high-vulnerability item subsets

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. If you have a pulse and an internet connection, companies want all the details they can get on what you're willing to buy -- and it's getting harder to tell where they're getting all that info. Retailers can now track what customers purchase to influence their buying patterns. Loyalty programs collect data on your purchases, frequency and preferences -- in exchange for deals.

Amazon's second Prime-related event for 2023 is officially called Prime Big Deal Days and will happen October 10 and 11. This is the second year in a row for a fall-based, site-wide Amazon sale and we're already seeing discounts pop up. You'll need a Prime membership to access many of the deals, though a few are available to everyone. This week, there are early Prime Day deals on the AirPods Pro, Amazon's Echo Dot, Amazon Music Unlimited, Eero 6 mesh Wi-Fi systems, Ring Video Doorbells and security systems and Amazon Fire Omni smart TVs. Here are the best early October Prime Day deals you can get right now.

It's Paris fashion week and the streets of the city are filled with celebrities, designers, models and journalists. Among the crowds, eagle-eyed experts are taking careful notes. These are the fashion industry's trend forecasters. Their job is to get a sense of the colours, cuts, fabrics and patterns in the designers' new collections, in the hope of detecting emerging trends. Their notes will quickly be added to curated "trend forecasts", which will be sold to designers and high street retailers, who will use them to inspire new pieces and decide what to stock next season – think of the "blue sweater" speech in The Devil Wears Prada, where Meryl Streep's character scathingly explains this process to her naive assistant Andy (played by Anne Hathaway).

Jeff Bezos' Blue Origin has spent two decades trailing Elon Musk's SpaceX in the space-exploration race. To fix this, Bezos has turned to a trusted Amazon.com Incoming Blue Origin CEO Dave Limp, who shepherded Alexa's introduction in 2014, is a fierce guardian of Amazon and Bezos' leadership principles, which put a premium on speed and solving customer problems. "Dave has an outstanding sense of urgency, brings energy to everything, and helps teams move very fast," Bezos told employees on Monday in an email seen by Bloomberg.

Amazon has a half-off deal for Prime members on a Blink outdoor / indoor security camera bundle. The sale gives you a pair of Blink Outdoor 4 cameras, which launched last month, and a Blink Mini for only $117.49. Whether these are your first security cameras or you're adding to an existing setup, this is a chance to save 50 percent off their usual cost. The Blink Outdoor 4 is a wireless device that, despite its name, can work as an inside or outside camera. It supports person detection, which uses computer vision to alert you when it spots a human in its field of view (if you also subscribe to an optional Blink subscription).

Evaluating open-domain dialogue systems is challenging for reasons such as the one-to-many problem, i.e., many appropriate responses other than just the golden response. As of now, automatic evaluation methods need better consistency with humans, while reliable human evaluation can be time- and cost-intensive. To this end, we propose the Reference-Assisted Dialogue Evaluation (RADE) approach under the multi-task learning framework, which leverages the pre-created utterance as reference other than the gold response to relief the one-to-many problem. Specifically, RADE explicitly compares reference and the candidate response to predict their overall scores. Moreover, an auxiliary response generation task enhances prediction via a shared encoder. To support RADE, we extend three datasets with additional rated responses other than just a golden response by human annotation. Experiments on our three datasets and two existing benchmarks demonstrate the effectiveness of our method, where Pearson, Spearman, and Kendall correlations with human evaluation outperform state-of-the-art baselines.

Amazon Prime members can save big on security cameras today. The retailer has a bundle including the Blink Video Doorbell and three Blink Outdoor 4 security cameras for 61 percent off. Get the Blink camera bundle for 61 percent off. You can connect the Blink Video Doorbell to your existing in-home chime or use it wirelessly. It supports two-way audio so you can hear and talk with whoever shows up on your front step.

Key challenges in running a retail business include how to select products to present to consumers (the assortment problem), and how to price products (the pricing problem) to maximize revenue or profit. Instead of considering these problems in isolation, we propose a joint approach to assortment-pricing based on contextual bandits. Our model is doubly high-dimensional, in that both context vectors and actions are allowed to take values in high-dimensional spaces. In order to circumvent the curse of dimensionality, we propose a simple yet flexible model that captures the interactions between covariates and actions via a (near) low-rank representation matrix. The resulting class of models is reasonably expressive while remaining interpretable through latent factors, and includes various structured linear bandit and pricing models as particular cases. We propose a computationally tractable procedure that combines an exploration/exploitation protocol with an efficient low-rank matrix estimator, and we prove bounds on its regret. Simulation results show that this method has lower regret than state-of-the-art methods applied to various standard bandit and pricing models. Real-world case studies on the assortment-pricing problem, from an industry-leading instant noodles company to an emerging beauty start-up, underscore the gains achievable using our method. In each case, we show at least three-fold gains in revenue or profit by our bandit method, as well as the interpretability of the latent factor models that are learned.

Understanding customer behavior in retail stores plays a crucial role in improving customer satisfaction by adding personalized value to services. Behavior analysis reveals both general and detailed patterns in the interaction of customers with a store items and other people, providing store managers with insight into customer preferences. Several solutions aim to utilize this data by recognizing specific behaviors through statistical visualization. However, current approaches are limited to the analysis of small customer behavior sets, utilizing conventional methods to detect behaviors. They do not use deep learning techniques such as deep neural networks, which are powerful methods in the field of computer vision. Furthermore, these methods provide limited figures when visualizing the behavioral data acquired by the system. In this study, we propose a framework that includes three primary parts: mathematical modeling of customer behaviors, behavior analysis using an efficient deep learning based system, and individual and group behavior visualization. Each module and the entire system were validated using data from actual situations in a retail store.