The government is launching an inquiry into the use of personal data to set individual prices for holidays, cars and household goods, amid rising fears of a consumer rip-off. The research, supported by the competition watchdog, will explore the prevalence of "dynamic pricing" based on information gathered about an individual, such as location, marital status, birthday or travel history. With about 17% of retail sales now made online, according to the Office for National Statistics, there is rising concern about the use of technology, including artificial intelligence and bots, to "personalise" prices, to the disadvantage of some shoppers. It has become common for online prices to fluctuate depending on time of day or availability – whether for gig tickets or Uber taxis. Now digital labels have begun to appear in shops, offering the potential to bring "surge pricing" into analogue sales.

As we round the corner to Amazon Alexa's third birthday, Amazon is posting deals on select Alexa-enabled devices to celebrate. A few other of our favorite retailers, like REI and Walmart, are beginning to post their holidays deals as well. As usual, we cruised through the web to bring you our favorite picks this week. REI Outlet is having a fantastic deep sale this weekend, and our pick are these polarized sunglasses from Smith. They may look like a pair of casual frames, but they have Smith's proprietary, polarized ChromaPop lenses, which optimize color, increase clarity, and reduce glare.

Just have a look at the recent scientific advancements. Technology has moved at a fast pace after the 1990s. In the good olden days, our ancestors have to wait for hundreds of years to know about fire. Then they invented the wheel. It is only after the Industrial Revolution technological innovations began to happen at a fast pace.

Retail companies that neglect machine learning do so at their peril. The name 1-800-Flowers.com is a charming legacy anachronism: These days, most of the gifting brand's customers don't dial a phone number, and a clear majority order more than bouquets. In fact, the now 40-plus-year-old parent, 1-800-FLOWERS.COM Inc., is today primarily an e-commerce business whose revenue, since its acquisitions of brands such as Harry & David, Cheryl's Cookies, Wolferman's, and The Popcorn Factory, comes largely from food-related gifts. Its floral origins notwithstanding, the company has been on the cutting edge when it comes to using machine learning (ML) to enhance customer experience. Since 2016, 1-800-FLOWERS.COM Inc. has launched several noteworthy marketing innovations to enhance the customer experience. Partnering with IBM Watson, the company introduced the AI-powered personal gift concierge GWYN (Gifts When You Need) to customize suggestions to online shoppers.

Bossa Nova, creator of real-time, on-shelf inventory robots for the retail industry, has opened a UK division in Sheffield. Bossa Nova provides retailers with data to redesign store operations and improve the customer shopping experience. Its robots drive autonomously through aisles, navigating safely among customers and store associates, and use AI to collect terabytes of data that retailers use to increase on-shelf availability. The robots are already in use in Walmart stores in the US. The company has not unveiled any planned partnerships with UK retailers yet, but Bossa Nova expects its UK office to grow rapidly.

Walmart's warehouse club, Sam's Club is preparing to open the doors at a new Dallas area store that will serve as a testbed for the latest in retail technology. Specifically, the retailer will test out new concepts like mobile checkout, an Amazon Go-like camera system for inventory management, electronic shelf labels, wayfinding technology for in-store navigation, augmented reality, and artificial intelligence-infused shopping, among other things. The retailer first announced its plans to launch a concept store in Dallas back in June, which was then said to be a real-world test lab for technology-driven shopping experiences. Today, the company is taking the wraps off the project and is detailing what it has planned for the new location, which goes by the name "Sam's Club Now." Like other Sam's Club stores, consumers will need a membership to shop at Sam's Club Now. But how they shop will be remarkably different.

Google has announced a partnership with Disney that can help bring kids stories to life. Starting today, Google Home can play ambient music and sound effects as you read select Little Golden Books aloud. All you have to do is say "Hey Google, let's read along to Disney," and start reading a compatible story. The announcement touts several features of the story-enhancing experience, including the ability for Google Home to detect when you stop reading or skip ahead. In those cases, the music or ambient sounds will adjust to fit where you are in the story.

Walmart's membership-only retail warehouse chain is prepping to take on Amazon with the launch of a new concept store that has no cashiers or registers. A Sam's Club in Dallas, Texas is testing a new store format where shoppers use an app to buy groceries without the need to stand in a checkout line, according to CNBC. The concept closely mirrors Amazon Go, the cashier-less stores launched last year by the internet giant. A Sam's Club in Dallas, Texas is testing a new store format where shoppers use an app to buy groceries without the need to stand in a checkout line or deal with cashiers The 32,000-square-foot store is smaller than other Sam's Club locations and mostly features basic food items like produce, meat and alcohol. For now, it's rolling out for select customers, but the firm expects to expand soon, CNBC said.

Breakthroughs in the application of complex calculations to large volumes of data have enabled machine-learning methodologies to revolutionize business processes in nearly every industry. Some of the more recognized examples of machine-learning applications include personalized Netflix recommendations and related product modules from online retailers such as Amazon and Nordstrom. However, there are less sexy yet equally impactful machine-learning examples, which include revenue management solutions used in hotels that incorporate these methodologies into an algorithmic engine to help produce pricing and inventory recommendations. Unlocking the potential of machine learning for the office of finance remains a hot topic for financial planning and analysis (FP&A) leaders, industry analysts, and technology vendors alike. Even more specifically, continuous chatter surrounds the ways that machine learning can improve future FP&A processes and how finance leaders can prepare for deploying advanced analytics within their organizations.

Recommender systems recommend items more accurately by analyzing users' potential interest on different brands' items. In conjunction with users' rating similarity, the presence of users' implicit feedbacks like clicking items, viewing items specifications, watching videos etc. have been proved to be helpful for learning users' embedding, that helps better rating prediction of users. Most existing recommender systems focus on modeling of ratings and implicit feedbacks ignoring users' explicit feedbacks. Explicit feedbacks can be used to validate the reliability of the particular users and can be used to learn about the users' characteristic. Users' characteristic mean what type of reviewers they are. In this paper, we explore three different models for recommendation with more accuracy focusing on users' explicit feedbacks and implicit feedbacks. First one is RHC-PMF that predicts users' rating more accurately based on user's three explicit feedbacks (rating, helpfulness score and centrality) and second one is RV-PMF, where user's implicit feedback (view relationship) is considered. Last one is RHCV-PMF, where both type of feedbacks are considered. In this model users' explicit feedbacks' similarity indicate the similarity of their reliability and characteristic and implicit feedback's similarity indicates their preference similarity. Extensive experiments on real world dataset, i.e. Amazon.com online review dataset shows that our models perform better compare to base-line models in term of users' rating prediction. RHCV-PMF model also performs better rating prediction compare to baseline models for cold start users and cold start items.