Figure 2 illustrates the distribution of research on various aspects of fairness in recommender systems.111The We may observe a division/split in the research on fairness-aware recommendation algorithms, with around 49.1% of papers concentrating on consumer fairness and somewhat fewer on producer fairness (41.8%). Few studies (less than 10%) address consumer and producer fairness concerns simultaneously. However, the underlying user-item interaction coexists on (and can impact) both sides of beneficiary stakeholders. For example, there may be disparities in the consumption of item groups (defined by protected attributes) between active and inactive users in specific domains.

Lenovo launched a new smart clock at CES earlier this year, and it improved upon its previous models by giving it a pogo docking pin at the bottom and support for Amazon's Alexa. If you've been thinking of picking it up but haven't gotten the chance to until now, you may want to head over to the device's listing at Best Buy. Lenovo's Smart Clock Essential with Alexa is currently on sale for $45 on the retailer's website only for today -- there's less than 20 hours left for the deal as of this writing. The Smart Clock Essential with Alexa retains the brand's Smart Clock 2 cloth design. While its predecessors only supported Google Assistant, though, this model only supports the Alexa voice assistant.

The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among other areas. This paper proposes a new algorithmic toolbox for tackling this problem through the lens of optimistic online learning. We build upon the Follow-the-Regularized-Leader (FTRL) framework which is developed further here to include predictions for the file requests, and we design online caching algorithms for bipartite networks with fixed-size caches or elastic leased caches subject to time-average budget constraints. The predictions are provided by a content recommendation system that influences the users viewing activity, and hence can naturally reduce the caching network's uncertainty about future requests. We prove that the proposed optimistic learning caching policies can achieve sub-zero performance loss (regret) for perfect predictions, and maintain the best achievable regret bound $O(\sqrt T)$ even for arbitrary-bad predictions. The performance of the proposed algorithms is evaluated with detailed trace-driven numerical tests.

Wendy Gonzalez is the CEO of Sama, the provider of accurate data for ambitious AI. Autonomous cars are as intrinsic to visions of the future as holograms and space travel. Since the birth of science fiction, the automobile has been seen as the final frontier of technological innovation. However, when we look around at our cities today, cars can often seem stuck in the past. The reality is that the vision for the automotive industry has far exceeded the pace of its progress.

It is natural for organizations to flatter their market rivals by copying their successful business strategies. In fact, corporate history is littered with examples of winning ideas being conveniently replicated. Take the now-overcrowded smart home assistant market, for example. Not many years after Amazon debuted the Echo in 2015, rival products such as Google Home and Apple HomePod arrived to compete for market supremacy. In a similar vein, organizations have seemingly understood the power of AI in the last few years.

While it may seem I'm just trying to work in as many buzzwords as I can, in fact, there really is an important intersection of these three elements. I've been interested in both big data and fast data for several years, and my newest tech interest is machine learning. As I have learned about the latter, I see that there are problems that require all three to be truly effective. One application for which I'm looking at bringing together these technologies is in Recommender Systems for brick and mortar shops. Probably the first big win for machine learning was Recommender Systems.

According to the most recent research paper produced by MarketsandResearch.biz, the Global Machine Learning Recommendation Algorithm Market is …

In recent years, players within Canada's financial services industry, from banks to Fintech startups, have shown early and innovative adoption of artificial intelligence ("AI") and machine learning ("ML") within their organizations and services. With the ability to review and analyze vast amounts of data, AI algorithms and ML help financial services organizations improve operations, safeguard against financial crime, sharpen their competitive edge and better personalize their services. As the industry continues to implement more AI and build upon its existing applications, it should ensure that such systems are used responsibly and designed to account for any unintended consequences. Below we provide a brief overview of current considerations, as well as anticipated future shifts, in respect of the use of AI in Canada's financial services industry. At a high level, Canadian banks and many bank-specific activities are matters of federal jurisdiction.

For all the modern conveniences technology brings to the home – Wi-Fi-enabled washing machines, powerful gaming systems and enormous smart televisions – one of the downsides is paying to power it all. In fact, home utility costs are continuing to spike for many parts of the country, with 2021 electricity prices rising at the fastest rate since 2008, says the U.S. Energy Information Administration (EIA) – already hitting Americans facing skyrocketing inflation, resulting in higher costs for many goods and services. Not only does the average household have dozens of consumer electronics products plugged into power outlets at any given time, most consume electricity when not in use. "Vampire power" – also referred to as "phantom power" or "standby power" – can account for as much as 10% of a household's electricity bill, says the Environmental Protection Agency (EPA). This can really add up.

But Musk's proposal likely represents a gross oversimplification of how it would work to make that data public, according to researchers who study recommendation algorithms. As social media companies have grown, the software that drives their recommendation engines have grown so sprawling and complex that analyzing it would require access to a fire hose of data so immense that most people wouldn't even have access to a powerful enough computer to analyze it. The algorithms at Twitter, Facebook and other social networks process billions of pieces of content and use countless datapoints to determining a ranking, from the popularity of a post to who posted it.