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.

User-item connected documents, such as customer reviews for specific items in online shopping website and user tips in location-based social networks, have become more and more prevalent recently. Inferring the topic distributions of user-item connected documents is beneficial for many applications, including document classification and summarization of users and items. While many different topic models have been proposed for modeling multiple text, most of them cannot account for the dual role of user-item connected documents (each document is related to one user and one item simultaneously) in topic distribution generation process. In this paper, we propose a novel probabilistic topic model called Prior-based Dual Additive Latent Dirichlet Allocation (PDA-LDA). It addresses the dual role of each document by associating its Dirichlet prior for topic distribution with user and item topic factors, which leads to a document-level asymmetric Dirichlet prior. In the experiments, we evaluate PDA-LDA on several real datasets and the results demonstrate that our model is effective in comparison to several other models, including held-out perplexity on modeling text and document classification application.

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.

The proliferation of mobile handheld devices in combination with the technological advancements in mobile computing has led to a number of innovative services that make use of the location information available on such devices. Traditional yellow pages websites have now moved to mobile platforms, giving the opportunity to local businesses and potential, near-by, customers to connect. These platforms can offer an affordable advertisement channel to local businesses. One of the mechanisms offered by location-based social networks (LBSNs) allows businesses to provide special offers to their customers that connect through the platform. We collect a large time-series dataset from approximately 14 million venues on Foursquare and analyze the performance of such campaigns using randomization techniquesand (non-parametric) hypothesis testing with statistical bootstrapping. Our main finding indicates that this type of promotions are not as effective as anecdote success stories might suggest. Finally, we design classifiers by extracting three different types of features that are able to provide an educated decision on whether a special offer campaign for a local business will succeed or not both in short and long term.

In this paper, we combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe decisions in OR/MS problems. In a departure from other work on data-driven optimization and reflecting our practical experience with the data available in applications of OR/MS, we consider data consisting, not only of observations of quantities with direct effect on costs/revenues, such as demand or returns, but predominantly of observations of associated auxiliary quantities. The main problem of interest is a conditional stochastic optimization problem, given imperfect observations, where the joint probability distributions that specify the problem are unknown. We demonstrate that our proposed solution methods are generally applicable to a wide range of decision problems. We prove that they are computationally tractable and asymptotically optimal under mild conditions even when data is not independent and identically distributed (iid) and even for censored observations. As an analogue to the coefficient of determination $R^2$, we develop a metric $P$ termed the coefficient of prescriptiveness to measure the prescriptive content of data and the efficacy of a policy from an operations perspective. To demonstrate the power of our approach in a real-world setting we study an inventory management problem faced by the distribution arm of an international media conglomerate, which ships an average of 1 billion units per year. We leverage both internal data and public online data harvested from IMDb, Rotten Tomatoes, and Google to prescribe operational decisions that outperform baseline measures. Specifically, the data we collect, leveraged by our methods, accounts for an 88% improvement as measured by our coefficient of prescriptiveness.