Online shopping and purchases are expected to rise by 78 percent this year as aggressive marketing of merchandise have risen higher than ever, a report says. "Unfazed by the slowdown, average online purchases are expected to increase by 78 percent in 2016 from 66 percent in 2015 due to attractive deals and aggressive marketing of an ever-expanding range of merchandise from clothes to jewellery, from electronics to books," said the Assocham-PwC report. With the growth of e-commerce sector, the possibilities and options for simplicity are what can bring multiple variations regardless of the prices floating in the online market. Searchub Inc., considering the prospect of the e-commerce and online shopping market developed a platform called Searchub that uses machine learning algorithms to offer users a truly personalized shopping experience. Connecting retailers with the customers through recommendation and big data, the platform performs search and displays result according to the user's taste, style and their social profile.

Robotics companies and academics descended on a resort in Palm Springs this week for an invitation-only conference organized by Amazon.com Inc. to bring together experts in artificial intelligence, robotics, home automation and space exploration. At the event, held at the Parker Palm Springs, there were robotic arms dueling with light sabers from Star Wars, seminars about imbuing machines with human values and a celebrity appearance by film director Ron Howard, known for his portrayal of Richie Cunningham in the sitcom Happy Days. Jeff Bezos mingled with attendees; one said he drank single-malt whiskey with Amazon's chief executive officer. Representatives from companies such as Rethink Robotics, Toyota Motor Corp., and others attended the conference, called MARS, short for Machine-Learning (Home) Automation, Robotics and Space Exploration.

An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets semantic patterns in consumers' discussions on social media. Specifically, the purposes of this study are twofold: 1) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities, two forms of positive vs. negative opinions and 2) driving actual content from the textual data that has a semantic trend. The considered tweets include consumers opinions on US retail companies (e.g., Amazon, Walmart). Cosine similarity and K-means clustering methods are used to achieve the former goal, and Latent Dirichlet Allocation (LDA), a popular topic modeling algorithm, is used for the latter purpose. This is the first study which discover semantic properties of textual data in consumption context beyond sentiment analysis. In addition to major findings, we apply LDA (Latent Dirichlet Allocations) to the same data and drew latent topics that represent consumers' positive opinions and negative opinions on social media.

Clarke told the Mumsnet marketing conference Mumstock that the key to his company remaining competitive against online retailers like Amazon and discounters Aldi and Lidl was to make the shopper experience easier and more convenient - whether in store or online. "The most consistent feedback we get on the store experience is to make life as easy as possible," said Clarke. "Customers want to park easily, get in easily, find everything they want and get out early. "And online, just make the experience easy. Don't deliver things I don't want.

They took the NYC Data Science Academy 12-week full-time data science bootcamp program from Sep. 23 to Dec. 18, 2015. The post was based on their fourth in-class project (due after the 8th week of the program). Walmart uses trip type classification to segment its shoppers and their store visits to better improve the shopping experience. Walmart's trip types are created from a combination of existing customer insights and purchase history data. The purpose of the Kaggle competition is to use only the purchase data provided to derive Walmart's classification labels.

No longer the exclusive domain of data-reliant businesses like Google, Microsoft, and Amazon, machine learning has been making its way into the masses as an essential approach to data. Today, machine learning is understood and accepted by a more mainstream audience, and has become a measurable driver for big business benefits both on and offline. There are three key reasons why machine learning has become one of the top 10 strategic technology trends that will shape digital business opportunities through 2020. First, the volume of data companies now collect is so massive that many companies struggle to make sense of it and fail to take advantage of it. Second, the computing power required to process these exploding data assets, previously exclusive to the Googles of this world, is now widely available to smaller businesses.

As retailers and brands predict and plan for the way consumers will shop in the future, artificial intelligence (AI) is high on the business development strategy for 2016 and beyond. Promising significant benefits for both retailers and consumers, AI is already around us and used everyday within shopping and payments. Businesses are embracing the benefits of the technology and progress within AI is accelerating at pace, with big things expected for the near, and distant, future. AI can process'big data' far more efficiently than humans and can recognise speech, images, text, patterns of online behaviour – for example to detect fraud – as well as appropriate advertisements for upselling. Smart machines and technology can turn data into customer insights and enhance service provisions, bringing the digital experience closer to the in-store interaction for consumers.

Alexa, what can you tell me about my health? Starting Thursday, Amazon's voice assistant will tell you how well you slept and how much more exercise you need -- at least if you have a Fitbit fitness tracker and an Alexa-compatible device, such as Amazon's Echo speaker and Fire TV streaming devices. Inc.'s answer to Apple's Siri, Google Now and Microsoft's Cortana -- is part of the online retailer's ambitions to control your living room, as people start embracing a "smart," automated home. You can already use voice commands to ask Alexa for weather, movie listings and sports scores. Ask about your sleep, and Alexa will tell you when you fell asleep and for how long.

Determinantal point processes (DPPs) have garnered attention as an elegant probabilistic model of set diversity. They are useful for a number of subset selection tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. In this work we present a new method for learning the DPP kernel from observed data using a low-rank factorization of this kernel. We show that this low-rank factorization enables a learning algorithm that is nearly an order of magnitude faster than previous approaches, while also providing for a method for computing product recommendation predictions that is far faster (up to 20x faster or more for large item catalogs) than previous techniques that involve a full-rank DPP kernel. Furthermore, we show that our method provides equivalent or sometimes better predictive performance than prior full-rank DPP approaches, and better performance than several other competing recommendation methods in many cases. We conduct an extensive experimental evaluation using several real-world datasets in the domain of product recommendation to demonstrate the utility of our method, along with its limitations.

We generally use hit rate to measure the performance of item recommendation algorithms. In addition to hit rate, we consider another two important factors which are ignored by most previous works. First, whether users are satisfied with the recommended items. It is possible that a user has bought an item but dislikes it. Hence high hit rate does not reflect high customer satisfaction. Second, whether the website retailers are satisfied with the recommendation results. If a customer is interested in two products and wants to buy one of them, it may be better to suggest the item which can help bring more profit. Therefore, a good recommendation algorithm should not only consider improving hit rate but also consider optimizing user satisfaction and website revenue. In this paper, we propose two algorithms for the above purposes and design two modified hit rate based metrics to measure them. Experimental results on 10 real-world datasets show that our methods can not only achieve better hit rate, but improve user satisfaction and website revenue comparing with the state-of-the-art models.