Recent years brought an increasing interest in the application of machine learning algorithms in e-commerce, omnichannel marketing, and the sales industry. It is not only to the algorithmic advances but also to data availability, representing transactions, users, and background product information. Finding products related in different ways, i.e., substitutes and complements is essential for users' recommendations at the vendor's site and for the vendor - to perform efficient assortment optimization. The paper introduces a novel method for finding products' substitutes and complements based on the graph embedding Cleora algorithm. We also provide its experimental evaluation with regards to the state-of-the-art Shopper algorithm, studying the relevance of recommendations with surveys from industry experts. It is concluded that the new approach presented here offers suitable choices of recommended products, requiring a minimal amount of additional information. The algorithm can be used in various enterprises, effectively identifying substitute and complementary product options.

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Ora Lassila is a Principal Graph Technologist in the Amazon Neptune graph database team. Earlier, he was a Managing Director at State Street, heading their efforts to adopt ontologies and graph databases. Before that, he worked as a technology architect at Pegasystems, as an architect and technology strategist at Nokia Location & Commerce (aka HERE), and prior to that he was a Research Fellow at the Nokia Research Center Cambridge. He was an elected member of the Advisory Board of the World Wide Web Consortium (W3C) in 1998-2013, and represented Nokia in the W3C Advisory Committee in 1998-2002. In 1996-1997 he was a Visiting Scientist at MIT Laboratory for Computer Science, working with W3C and launching the Resource Description Framework (RDF) standard; he served as a co-editor of the RDF Model and Syntax specification.

Why Will It Affect You? How Do You Survive and Thrive through the AI Revolution? How Do You Survive and Thrive through the AI Revolution? "A fresh, thought provoking, entertaining and accessible post pandemic account of the present and future impact of AI and how to live with it, packed full of useful facts and quotable analogies and anecdotes." "A fresh, thought provoking, entertaining and accessible post pandemic account of the present and future impact of AI and how to live with it, packed full of useful facts and quotable analogies and anecdotes."

The consumer goods retail industry is facing a fundamental dilemma. There is no historical data that can guide today's strategy, but market-standard models for pricing and promotions have typically relied on regressing the past. The industry has long leveraged historical data to extrapolate what might work today, and while there may be value in that analysis, what worked yesterday may not fit today's context. If the past two years have taught us anything, it's that "now" is always changing. The dynamics at play in today's environment quickly reveal why the techniques and algorithms the industry has traditionally deployed when it comes to prices and promotions are no longer enough.

Computational Learning Theory: 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings (Lecture Notes in Computer Science, 2375) [Kivinen, Jyrki, Sloan, Robert H.] on Amazon.com. *FREE* shipping on qualifying offers. Computational Learning Theory: 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings (Lecture Notes in Computer Science, 2375)

In order to steer e-commerce users towards making a purchase, marketers rely upon predictions of when users exit without purchasing. Previously, such predictions were based upon hidden Markov models (HMMs) due to their ability of modeling latent shopping phases with different user intents. In this work, we develop a duration-dependent hidden Markov model. In contrast to traditional HMMs, it explicitly models the duration of latent states and thereby allows states to become "sticky". The proposed model is superior to prior HMMs in detecting user exits: out of 100 user exits without purchase, it correctly identifies an additional 18. This helps marketers in better managing the online behavior of e-commerce customers. The reason for the superior performance of our model is the duration dependence, which allows our model to recover latent states that are characterized by a distorted sense of time. We finally provide a theoretical explanation for this, which builds upon the concept of "flow".

I will make the argument that the disciplines of math and statistics have captured mainstream interest because of the growing availability of data, and we need math, statistics, and machine learning to make sense of it. Yes, we do have scientific tools, machine learning, and other automations that call to us like sirens. We blindly trust these "black boxes," devices, and softwares; we do not understand them but we use them anyway. While it is easy to believe computers are smarter than we are (and this idea is frequently marketed), the reality cannot be more the opposite. This disconnect can be precarious on so many levels.

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs.

Thanks to the rise of new technologies, the world we live in today is vastly different from the world we lived in, even just a few years ago. One of the most profound changes the internet has brought about is the rise of the internet of things (IoT). The IoT refers to the network of physical devices connected to the internet that can collect, store, share, and interpret data. With IoT in mind, one of the biggest trends in the tech world is the rise of Artificial Intelligence. The AI technology has been used in everything from computers to medical devices, and it will only become more prevalent in our everyday lives.