The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e.g., users, content providers, vendors) in the recommender ecosystem have drawn increasing attention in recent years. Developing and training models and algorithms for such recommenders can be especially difficult using static datasets, which often fail to offer the types of counterfactual predictions needed to evaluate policies over extended horizons. To address this, we develop RecSim NG, a probabilistic platform for the simulation of multi-agent recommender systems. RecSim NG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a powerful, general probabilistic programming language for agent-behavior specification; tools for probabilistic inference and latent-variable model learning, backed by automatic differentiation and tracing; and a TensorFlow-based runtime for running simulations on accelerated hardware. We describe RecSim NG and illustrate how it can be used to create transparent, configurable, end-to-end models of a recommender ecosystem, complemented by a small set of simple use cases that demonstrate how RecSim NG can help both researchers and practitioners easily develop and train novel algorithms for recommender systems.

The education industry is one of the most thriving sectors in India. While the pandemic brought with it various challenges, it also helped in digital adoption across all businesses. This trend was especially witnessed in the education sector. The teachers and students had to cope up with the changing times and had to move the learning sessions to the online medium. To ensure that the educational firms and the ed-tech platforms extend the best of the knowledgeable sessions to their students, they need to be armed with state-of-the-art technology.

Apple's HomePod smart speaker will be discontinued according to a statement the company provided to TechCrunch this evening. Existing users will receive software updates and support through Apple Care according to the company. On the U.S. website, the space gray color is listed as "Sold Out" but there are still models available in white. However, this move will not signal the end of the HomePod product line. Apple's HomePod Mini will continue to be sold. HomePod mini has been a hit since its debut last fall, offering customers amazing sound, an intelligent assistant, and smart home control all for just $99.

Apple will discontinue its original HomePod four years after first releasing the smart speaker. The Cupertino, California-based tech giant says it will instead focus on its new and smaller HomePod mini, which went on sale in November for $99. "We are discontinuing the original HomePod, it will continue to be available while supplies last through the Apple Online Store, Apple Retail Stores and Apple Authorized Resellers," Apple said in a statement, reported by TechCrunch. "We are focusing our efforts on HomePod mini." Apple didn't immediately respond Saturday to USA TODAY's request for comment.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Maybe you rely on Siri or the assistant built into your phone, but you likely have a full-fledged AI assistant in your home too. Alexa, built into the Amazon Echo, is everywhere. If you have an Echo, there's a good chance Alexa has driven you up the wall a time or two with the follow-up questions.

The recommendation system workflow shown in the diagram above shows the user's collaboration regarding the ratings of different movies or shows. New users get their recommendations based on the recommendations of existing users. Recommender systems are machine learning-based systems that scan through all possible options and provides a prediction or recommendation. Content filtering expects the side information such as the properties of a song (song name, singer name, movie name, language, and others.). Recommender systems perform well, even if new items are added to the library.

Individual investors are now massively using online brokers to trade stocks with convenient interfaces and low fees, albeit losing the advice and personalization traditionally provided by full-service brokers. We frame the problem faced by online brokers of replicating this level of service in a low-cost and automated manner for a very large number of users. Because of the care required in recommending financial products, we focus on a risk-management approach tailored to each user's portfolio and risk profile. We show that our hybrid approach, based on Modern Portfolio Theory and Collaborative Filtering, provides a sound and effective solution. The method is applicable to stocks as well as other financial assets, and can be easily combined with various financial forecasting models. We validate our proposal by comparing it with several baselines in a domain expert-based study.

We introduce an approach to recommending short-lived dynamic packages for golf booking services. Two challenges are addressed in this work. The first is the short life of the items, which puts the system in a state of a permanent cold start. The second is the uninformative nature of the package attributes, which makes clustering or figuring latent packages challenging. Although such settings are fairly pervasive, they have not been studied in traditional recommendation research, and there is thus a call for original approaches for recommender systems. In this paper, we introduce a hybrid method that leverages user analysis and its relation to the packages, as well as package pricing and environmental analysis, and traditional collaborative filtering. The proposed approach achieved appreciable improvement in precision compared with baselines.

COVID-19 has undoubtedly accelerated the application of AI in the healthcare industry, such as virus surveillance, diagnosis, and patient risk assessments. AI-powered robots and digital assistants with real-time monitoring and analysis have enabled doctors to provide more effective and personalized treatment. Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. It gives algorithms the ability to "learn" from training data so as to identify patterns and make decisions with little human intervention.

Group recommender systems facilitate group decision making for a set of individuals (e.g., a group of friends, a team, a corporation, etc.). Many of these systems, however, either assume that (i) user preferences can be elicited (or inferred) and then aggregated into group preferences or (ii) group preferences are partially observed/elicited. We focus on making recommendations for a new group of users whose preferences are unknown, but we are given the decisions/choices of other groups. By formulating this problem as group recommendation from group implicit feedback, we focus on two of its practical instances: group decision prediction and reverse social choice. Given a set of groups and their observed decisions, group decision prediction intends to predict the decision of a new group of users, whereas reverse social choice aims to infer the preferences of those users involved in observed group decisions. These two problems are of interest to not only group recommendation, but also to personal privacy when the users intend to conceal their personal preferences but have participated in group decisions. To tackle these two problems, we propose and study DeepGroup -- a deep learning approach for group recommendation with group implicit data. We empirically assess the predictive power of DeepGroup on various real-world datasets, group conditions (e.g., homophily or heterophily), and group decision (or voting) rules. Our extensive experiments not only demonstrate the efficacy of DeepGroup, but also shed light on the privacy-leakage concerns of some decision making processes.