On September 29, 2021, the United States and the European Union's (EU) new Trade and Technology Council (TTC) held their first summit. It took place in the old industrial city of Pittsburgh, Pennsylvania, under the leadership of the European Commission's Vice-President, Margrethe Vestager, and U.S. Secretary of State Antony Blinken. Following the meeting, the U.S. and the EU declared their opposition to artificial intelligence (AI) that does not respect human rights and referenced rights-infringing systems, such as social scoring systems.1 During the meeting, the TTC clarified that "The United States and European Union have significant concerns that authoritarian governments are piloting social scoring systems with an aim to implement social control at scale. These systems pose threats to fundamental freedoms and the rule of law, including through silencing speech, punishing peaceful assembly and other expressive activities, and reinforcing arbitrary or unlawful surveillance systems."2 The implicit target of the criticism was China's "social credit" system, a big data system that uses a wide variety of data inputs to assess a person's social credit score, which determines social permissions in society, such as buying an air or train ticket.3 The critique by the TTC indicates that the U.S. and the EU disagree with China's view of how authorities should manage the use of AI and data in society.4

You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine.

When I open any website offering services or goods, I always check how well a recommender system works. Big business also adores recommender engines as much as I do, so I am in good company. "Recommender engines or recommenders, as they are sometimes called, are the most useful applications of Machine Learning Algorithms." – Harvard Business Review. And they help me to choose another plant to the disappointment of my husband ( "One more plant? We have a dozen of them already!").

First published on my Substack. In 2017, I had two choices: AI or Crypto. There were 2 job offers in front of me. One was a software company that specialized in AI-driven decisioning, and one was a startup in the emerging field of cryptocurrency and was building something on the blockchain protocol.

We have gobs of data, nearly limitless cloud compute, and ever-improving machine learning algorithms, so what on earth is holding companies back from succeeding with big data? "Talent, talent, talent," says Dr. Kirk Borne. "The limiting factor is talent." To be sure, Borne has done more than most when it comes to fostering data science talent. Fourteen years ago, before his recent stint at Booz Allen Hamilton or his new gig at DataPrime, Borne helped create the nation's first data science degree program at George Mason University.

Application service providers manage huge and complex infrastructures. Like any complex systems, things could go wrong from time to time, due to various reasons (for example, network connection response problems, infrastructure resource limitations, software malfunctioning issues, and so on). As a result, the question of how to quickly resolve issues when they happen becomes critical to help improve customer satisfaction and retention. Note: Performance numbers claimed in this post are based on public data sets and not specific to a particular project or organization. Recently, the fast advancement of natural language processing (NLP) algorithms have helped solve many practical problems by analyzing text information.

Recommender systems are among the most fun and profitable applications of data science in the big data world. Training data (corresponding to the historical search, browse, purchase, and customer feedback patterns of your customers) can be converted into golden opportunities for ROI (i.e., Return On Innovation and Investment). The predictive analytics tools of data science yield a bonanza of mechanisms to engage your customers and enrich their customer experience. What better loyalty program can there be if not the one that offers the customer what they want before they ask (and sometimes, even before they think of it for themselves). Yes, we know of some cases that have gone bad (such as the secretly pregnant teen and the targeted coupons that Target sent to her father), and we recognize that there is a fine line between being intimate with your customers versus being intimidating, but usually people do like to receive offers for great products that they love.

It's a truism that we live in a "digital age". It would be more accurate to say that we live in an algorithmically curated era – that is, a period when many of our choices and perceptions are shaped by machine-learning algorithms that nudge us in directions favoured by those who employ the programmers who write the necessary code. A good way of describing them would be as recommender engines. They monitor your digital trail and note what interests you – as evidenced by what you've browsed or purchased online. Amazon, for example, regularly offers me suggestions for items that are "based on your browsing history".

In 2006, Netflix, which was then a DVD rental service, announced a data science competition for movie rating predictions. The company would offer a $1 million grand prize to the team that could improve their existing recommender system's prediction accuracy by 10%. The competition garnered much interest from researchers and engineers in both academia and industry. Within the first year of the competition, over 40,000 teams from more than 100 countries had entered the competition [1]. In June 2009, the prize was awarded to BellKor's Pragmatic Chaos, a team of AT&T engineers, who submitted the winning algorithm a few minutes earlier than the second-place team [2].

Ted Dunning (Chief Application Architect at MapR) and Ellen Friedman have written a new O'Reilly Media book on _"Practical Machine Learning – Innovations in Recommendation" _(released in January 2014). This book examines one of the most interesting, fun, and powerful data science applications in the big data universe: recommendation systems. For me, this was one of the most interesting applications of data mining that immediately captured my imagination after I embarked on the journey to data science (drifting away from my astrophysics roots) about a dozen years ago. It is also one of the most common use cases that are taught in data science MOOCs and other analytics training courses. I believe that the love affair with recommender systems can be partly attributed to two things.