Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often expect their recommendations to be purely personalized, these new algorithmic objectives must be communicated transparently in a fairness-aware recommender system. While explanation has a long history in recommender systems research, there has been little work that attempts to explain systems that use a fairness objective. Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation. Here, we consider user perspectives of fairness-aware recommender systems and techniques for enhancing their transparency. We describe the results of an exploratory interview study that investigates user perceptions of fairness, recommender systems, and fairness-aware objectives. We propose three features -- informed by the needs of our participants -- that could improve user understanding of and trust in fairness-aware recommender systems.

Now's a good time to buy a smart speaker -- particularly if Daylight Saving Time had you searching for a better alarm. Amazon is offering Gold Box deals on several Echo devices for today (March 14th) only. Displays have received the largest discounts, with the alarm clock-like Echo Show 5 on sale for $50 (down from $90) and the larger Echo Show 8 dipping to $80 (previously $130). The current-generation Echo Dot has dropped to $35 (formerly $50), while its clock-equipped version is $45 (normally $60). And if you'd prefer something more powerful, the standard Echo is on sale for $80 instead of its usual $100.

Artificial intelligences (AI) are increasingly being embodied and embedded in the world to carry out tasks and support decision-making with and for people. Robots, recommender systems, voice assistants, virtual humans - do these disparate types of embodied AI have something in common? Here we show how they can manifest as "socially embodied AI." We define this as the state that embodied AI "circumstantially" take on within interactive contexts when perceived as both social and agentic by people. We offer a working ontology that describes how embodied AI can dynamically transition into socially embodied AI. We propose an ontological heuristic for describing the threshold: the Tepper line. We reinforce our theoretical work with expert insights from a card sort workshop. We end with two case studies to illustrate the dynamic and contextual nature of this heuristic.

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.