The financial services industry has seen a great deal of disruption from digital-based alternatives. Many of these challengers use advanced technology and expanded data sets to offer apps that provide financial solutions at a lower cost, with less friction and greater personalization than traditional bank or credit union offerings. Toronto-based startup Flybits believes that the best way to compete in the future is not just by developing innovative products and services, but by becoming the repository of choice for data in addition to money. "I definitely see that banks are in a perfect position, if they innovate right, to be the perfect data vaults for the future – managing the privacy and also the data of their customers," says Hossein Rahnama, CEO and Co-Founder of Flybits, in an exclusive interview for Banking Transformed, a new podcast from Jim Marous and The Financial Brand. "Using AI and machine learning, there is the potential to build a'data marketplace' for banks, fintechs and other data providers to partner and build more services together."

Microsoft's listening program continues to grow in scope after a new report reveals that contractors harvested unintentional audio from Xbox users through Cortana and the Kinect. Motherboard reports that Xbox users were recorded by Microsoft as part of a program to analyze users' voice-commands for accuracy and that those recordings were assessed by human contractors. While the program was designed to only scrape audio uttered after a wake-word, contractors hired by Microsoft report that some recordings were taken accidentally without provocation. The practice, reports Motherboard, has been ongoing for several years since the early days of Xbox One and predates Xbox's integration with its voice assistant, Cortana. Xbox users were being recorded by Microsoft in a listening program that scraped audio from Cortana and its augmented reality hardware, Kinect. The company analyzed commands given to the Xbox's increasingly unpopular augmented reality hardware called the Kinect.

Given a sparse rating matrix and an auxiliary matrix of users or items, how can we accurately predict missing ratings considering different data contexts of entities? Many previous studies proved that utilizing the additional information with rating data is helpful to improve the performance. However, existing methods are limited in that 1) they ignore the fact that data contexts of rating and auxiliary matrices are different, 2) they have restricted capability of expressing independence information of users or items, and 3) they assume the relation between a user and an item is linear. We propose DaConA, a neural network based method for recommendation with a rating matrix and an auxiliary matrix. DaConA is designed with the following three main ideas. First, we propose a data context adaptation layer to extract pertinent features for different data contexts. Second, DaConA represents each entity with latent interaction vector and latent independence vector. Unlike previous methods, both of the two vectors are not limited in size. Lastly, while previous matrix factorization based methods predict missing values through the inner-product of latent vectors, DaConA learns a non-linear function of them via a neural network. We show that DaConA is a generalized algorithm including the standard matrix factorization and the collective matrix factorization as special cases. Through comprehensive experiments on real-world datasets, we show that DaConA provides the state-of-the-art accuracy.

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}.