It's time to revisit the discussion on recommendation engines. In this installment, I'm going to provide you a conceptual overview of the topic, and then, following that I'll show you how to build a recommendation engine in R. Ready? Before showing you how to build a recommendation engine in R, I need to get you up-to-speed on the concepts behind how recommendation engines work. In case you're totally new to marketing data science, let me illustrate the recommendation engine concept a little before proceeding. You know how, when you go buy something on Amazon, you see related products under the heading of'People who purchased this item also purchased…' (or something like that).

Many women use dating apps like Tinder and Bumble to confirm their attractiveness rather than find a partner. New research into our swiping habits habits has found that men swipe with an eye for casual sex while girls prefer to use dating apps for an ego boost. This is because women get a kick out of being perceived as a potential partner by other users, scientists said. 'Women use dating apps to feel better about themselves more than men do,' said study coauthor Dr Mons Bendixen, from the Norwegian University of Science and Technology in Trondheim. Lead author Ernst Olav Botnen added: 'Men tend to report a desire for casual sex and short-term relationships as a reason for using dating apps.

If Google I/O 2017 was all about Google's restructuring effort and the resonance of artificial intelligence brewing in Google Labs, I/O 2018 was more about humanizing those AI efforts by integrating them into its line of products, particularly Google Assistant, which powers Google Home and is an integral part of a bunch of other Google and non-Google devices. Google Assistant itself was the successor to Google Now. It enabled Android users, for the first time, to engage in a two-way conversation with their Android devices, something iOS users were already enjoying with Siri. During I/O 2016 while introducing Google Assistant, CEO Sundar Pichai called it, "The future of computing as an ambient experience that extends beyond devices." Two of the very big Google launches at the event -- Google Allo, a messaging app with a resident chatbot, and Google Home, a smart speaker to rival Amazon Echo powered by Alexa -- were both powered by Google Assistant, an outcome of Google's continual efforts in natural language processing, deep learning, and text-to-speech over the years.

Back in November of 2015, Google officially confirmed they had implemented a machine learning AI called RankBrain into their query-filtering process. Rankbrain learns from each user query and applies these learnings to each successive query. Machine learning AI helps Google recognize the natural language people use when they type online and then use this information to provide the most relevant search results. The goal is to optimize the user experience by providing nothing but the highest quality information. RankBrain will play a large role in processing voice search queries as the popularity of home assistants like Alexa and Google Home continue to rise.

LinkedIn made it passé to send dozens of résumés to potential employers. Now Debut is finding the perfect match for graduates with personality tests and math quizzes, degrees be damned. It's a British competitor to New York-based WayUp, and investors here seem to like its targeted approach to recruitment: the startup has just raised $6.7 million (£5 million) to make its recommendation algorithms for job-seeking graduates smarter. Debut already has 60 corporate clients, including Google, Apple and Barclays, that pay it an annual subscription to parse the data flood of graduate applicants who have completed psychometric and personality tests on its app. "Most jobs applications have generic information," says founder Charlie Taylor, a former consultant at Ernst & Young who also ranked on Forbes' 30 Under 30 in European tech in 2017.

Microsoft has bought Semantic Machines, an artificial intelligence start-up, as it looks to boost its efforts in developing conversational AI. Berkeley, California-based Semantic's approach to AI is using machine learning to add context to conversations with chatbots. This means taking information received by AI and applying it to future dialogue. The firm's speech recognition team previously led automatic speech recognition development for Apple's personal assistant Siri. In its announcement Monday, Microsoft did not disclose any financial details of the acquisition.

Last week's big news from Facebook's (FB) F8 conference was Facebook's long-anticipated foray into the online dating game. Alphabet's (GOOG) Google I/O is happening this week in Mountain View, California and featured an impressive demo of its state-of-the-art AI capabilities. Here in Seattle, the "counter-programming" as it were, is the Microsoft Build 2018 Developer Conference. Perhaps as something of a direct response to Facebook's recent high-profile issues with data privacy as well as Google's own troubled past on this topic, Microsoft (MSFT) CEO Satya Nadela in his opening keynote pointedly addressed the issue of civic and ethical responsibility for technology companies. This underscores the message he gave in a recent interview on NBC's Today Show, where he said that'Trust' will push the company ahead of Amazon (NASDAQ:AMZN) and Google.

Microsoft today announced that it has acquired Semantic Machines to bolster its conversational AI offerings -- like Cortana, the Azure Bot Service, and Microsoft Cognitive Services. Semantic Machines works in areas like speech synthesis, deep learning, and natural language processing. Semantic Machines describes itself as a company bent on creating conversational AI that enables machines "to communicate, collaborate, understand our goals, and accomplish tasks." It could help Microsoft compete with conversational computing initiatives from Amazon's Alexa, Apple's Siri, Google's Assistant, and Samsung's Bixby. In addition, Semantic Machines has assembled a cadre of experts in the conversational AI arena, like Larry Gillick, former chief scientist for Siri at Apple, and well-known researchers like UC Berkeley professor Dan Klein and Stanford University professor Percy Liang.

Considering its work with Cortana, Microsoft's acquisition of Semantic Machines makes perfect sense. Announced Sunday, the purchase is designed to bolster not just Microsoft digital voice assistant Cortana but also social chatbots like XiaoIce, which has had up to 30 billion conversations across China, Japan, the United States, India and Indonesia. Berkeley, California-based Semantic Machines describes itself as developing the fundamental technology to allow humans to interact naturally with computers. It's led by tech entrepreneur Dan Roth, UC-Berkeley Professor Dan Klein and Stanford University Professor Percy Liang. "With the acquisition of Semantic Machines, we will establish a conversational AI center of excellence in Berkeley to push forward the boundaries of what is possible in language interfaces," said David Ku, chief technology officer of Microsoft AI & Research. "Combining Semantic Machines' technology with Microsoft's own AI advances, we aim to deliver powerful, natural and more productive user experiences that will take conversational computing to a new level."

In recent months, edge computing has become a buzzword. But what exactly is edge computing? Let's time travel back 10 years to the era of personal computing. Back then, we typed our documents in Word, listened to music and played media from CDs we bought and stored our photos on the computer hard drive. Our one personal computer- a desktop or laptop, was the central hub of every computing activity we did back then.