Recommender Systems are nowadays successfully used by all major web sites (from e-commerce to social media) to filter content and make suggestions in a personalized way. Academic research largely focuses on the value of recommenders for consumers, e.g., in terms of reduced information overload. To what extent and in which ways recommender systems create business value is, however, much less clear, and the literature on the topic is scattered. In this research commentary, we review existing publications on field tests of recommender systems and report which business-related performance measures were used in such real-world deployments. We summarize common challenges of measuring the business value in practice and critically discuss the value of algorithmic improvements and offline experiments as commonly done in academic environments. Overall, our review indicates that various open questions remain both regarding the realistic quantification of the business effects of recommenders and the performance assessment of recommendation algorithms in academia.

Artifical Intelligence, the e-commerce industry can improve customer experience with personalization, targeting potential customers to increase sales, and recommending them products based on their purchase and browsing behavior. According to an article published by Business Insider, early 85% of all customer interactions is going to be managed without human support by 2020. Considering this advancing trend, many e-commerce businesses have begun to use different forms of artificial intelligence technology for understanding their customers better, offering them the best user experience, and generating more sales and revenues. Often it happens that the customers, after browsing the e-commerce website for a while, abandon their search and leave the website. This generally happens when the customers are not able to find enough relevant product results. In such scenarios, AI can help a business with an intelligent solution.

The rapid progress in artificial intelligence, smart devices, and smart cities promises to revolutionise the way we work, live, and connect. However, recent scandals surrounding the handling of user data have prompted a wave of privacy concerns. The smarter a city gets, the more it can keep tabs on our every move. Likewise, with connected home devices and digital assistants picking up our daily activities and queries, the potential for privacy breaches are endless. Europe's pioneering General Data Privacy Regulation (GDPR) is one of several attempts by governments to mitigate widespread shortfalls in customer data protection, for both companies and governments. Other countries, and even US states like California, have followed.

A new robot project has been published to the Instructables Circuits website which is equipped with machine learning technology allowing it to see the world using a generic camera to perform tasks depending on the detected object's position and orientation. Check out the video below to learn more about the Raspberry Pi powered robot which is equipped with a 3D printed claw. "This robot is truly special because it can use Machine Learning models to'see' the world via a generic camera and perform tasks depending on how the detected object's position is changing in the camera. This robot is built around the ever popular Raspberry pi, the incredibly powerful RoboClaw motor controller, and the common Rover 5 robot platform. Furthermore, all the additional physical parts are 3D printed.

One of the first things to do while planning a trip is to book a good place to stay. Booking a hotel online can be an overwhelming task with thousands of hotels to choose from, for every destination. Motivated by the importance of these situations, we decided to work on the task of recommending hotels to users. W e used Expedia's hotel recommendation dataset, which has a variety of features that helped us achieve a deep understanding of the process that makes a user choose certain hotels over others. The aim of this hotel recommendation task is to predict and recommend five hotel clusters to a user that he/she is more likely to book given hundred distinct clusters.

Self-reinforcing feedback loops in personalization systems are typically caused by users choosing from a limited set of alternatives presented systematically based on previous choices. We propose a Bayesian choice model built on Luce axioms that explicitly accounts for users' limited exposure to alternatives. Our model is fair---it does not impose negative bias towards unpresented alternatives, and practical---preference estimates are accurately inferred upon observing a small number of interactions. It also allows efficient sampling, leading to a straightforward online presentation mechanism based on Thompson sampling. Our approach achieves low regret in learning to present upon exploration of only a small fraction of possible presentations. The proposed structure can be reused as a building block in interactive systems, e.g., recommender systems, free of feedback loops.

Items in modern recommender systems are often organized in hierarchical structures. These hierarchical structures and the data within them provide valuable information for building personalized recommendation systems. In this paper, we propose a general hierarchical Bayesian learning framework, i.e., \emph{HBayes}, to learn both the structures and associated latent factors. Furthermore, we develop a variational inference algorithm that is able to learn model parameters with fast empirical convergence rate. The proposed HBayes is evaluated on two real-world datasets from different domains. The results demonstrate the benefits of our approach on item recommendation tasks, and show that it can outperform the state-of-the-art models in terms of precision, recall, and normalized discounted cumulative gain. To encourage the reproducible results, we make our code public on a git repo: \url{https://tinyurl.com/ycruhk4t}.

When you really think about it, what we call "work" hasn't evolved much since the Industrial Revolution: While the technology has made most tasks easier, the name of the game is still mass production--codifying business processes using technology and scaling it across the entire enterprise. But artificial intelligence (AI) changes the game. Rather than fitting people into a repetitive process, AI offers the opportunity to liberate them from it altogether, freeing their time and creative energy for more strategic and meaningful work. For the first time since the Industrial Revolution, technology is being used to bring humanity back to work. This revolution actually began at home, thanks to the smart technology behind Siri, Alexa, and Google Assistant, which have found a place in our daily lives.

Have you ever wondered whom to thank for some of the modern conveniences you might have started taking for granted, like Siri, Cortana or Alexa (assuming you agree these are conveniences)? The people at the Association for Computing Machinery (ACM) decided to thank Geoffrey Hinton, Yoshua Bengio and Yann LeCun in April of this year by honoring them with the Turing Award for their contributions to deep learning and neural networks. These contributions are put to use every time you log into your smartphone using fingerprint or facial recognition or when you use Google Photos or a voice assistant, and likely every time you use Amazon, Netflix, Facebook or Instagram. The advances in automatic language translation and autonomous cars in recent years arguably wouldn't have progressed as rapidly had it not been for the contributions of these three researchers. All of that is still an understatement of their contributions to artificial intelligence (AI).

Artificial Intelligence used to be a fictive narrative from Isaac Asimov's novels and futuristic films. Today it's 2019, and AI is no longer just a product of imagination. Let's take a look at the world's favorite futuristic movies. We already have them in every household. These can be easily found on Amazon and even your local Home Depot.