Recommendation systems play an important role in many e-commerce applications as well as search and ranking services [6, 15, 21, 26, 30, 31, 41, 48]. There are two main strategies to perform recommendations: content and collaborative filtering. In content filtering the user creates a profile based on its interest, while human experts create a profile for the product. An algorithm matches the two profiles and recommends the closest matches to the user. For example, this approach is taken by the Pandora Music Genome Project [29]. In collaborative filtering, the recommendations are based only on user past behavior from which the future behavior is derived. The advantage of this approach is that it requires no external information and is not domain specific. The challenge is that in the beginning very few user-item interactions are available. For instance, this cold start problem is addressed by Netflix by asking the user for a few favorite movies when creating their profile for the first time [27].

There are several ways technology betters our day-to-day lives. Our phones, cars, and computers are becoming smarter every day. With a few buttons, you can order food, set up doctor consultations, and even be driven to another place. We are now used to living in that version of our world. But do we ask ourselves how they keep advancing? More often than not, the reason for this is AI. But what is AI, and what are some examples of AI that we don't know we're using? There is not a universal definition of artificial intelligence (AI). Some define it through its field of study; others define it by how "human" its qualities are. When these definitions are added together, you get an idea of what AI is all about. AI is a branch of the computer sciences that develops machines into thinking and acting like humans. Machines, when built with AI, must think rationally and act humanly. Recent developments in AI have made them capable of speech recognition, problem-solving, and machine-learning. As you can see from these examples, humans are replicating what makes them distinct from other animals.

If there's one thing the pandemic hasn't canceled, it's the search for love. Throughout the health emergency, daters have taken to apps, websites and matchmaking services in search of connection, with more meeting in person as the crisis drags on at a time when every touch is calculated and fraught. They're feeling resilient, and they're not willing to put a year of their love life on hold because of the global pandemic," said Logan Ury, chief researcher for the popular dating app Hinge. In March, Hinge experienced a 30% increase over January and February in messages sent among users. In June, compared to the same month last year, there was a 13% increase in the number of dates -- virtual and in person -- in the U.S. and U.K., Ury said. Some daters insist on safety precautions before leaping into offline meetups. Others take no precautions, relying on mutual trust. A lucky few are on the ultimate step, marriage. Look no further than Jordan and Brittany Tyler in Allegan, Mich., as evidence of that. Jordan, an adjunct professor of communications at Western Michigan University, and Brittany, who supervises a program for autistic youth, had both been divorced about a year when the pandemic hit. Neither had dated online before they signed up for Match.com. "When the lockdown happened an alert went off on my phone and it sounded liked'The Purge' or something," Brittany laughed. "I thought, 'I'm going to die alone.'" Both had dated their exes for several years before marrying. The two started texting March 18. They were wed by July after spending much of quarantine together after a romantic date March 24 at Jordan's place. He made gluten-free pasta from scratch and threw steaks on the grill. They watched the movie "P.S.

The use of Conversational Artificial Intelligence (AI) such as Chatbots and Intelligent Automation, would enhance customer interaction, and renewing of business model and operations. The global Pandemic has rendered many organizations to shift working remotely. The earlier scenario of working in an office, while complying to customers expectations has now been transformed over digital platforms. At the same time, the added burden of compliance with those demands in a coordinated manner has pushed many organizations to adopt solutions digitally. As organizations are looking for alternatives that would assist them to manage the business, conversational AI can be observed as an emerging choice.

Timed Practice Exam is coming soon! New reference architecture section with hands-on lab that demonstrates how to build a data lake solution using AWS Services and the best practices: 2020 AWS S3 Data Lake Architecture. This topic covers essential services and how they work together for a cohesive solution. AWS Artificial Intelligence material is now live! Within a few minutes, you will learn about algorithms for sophisticated facial recognition systems, sentiment analysis, conversational interfaces with speech and text and much more.

In this article, I use the Kaggle Netflix prize data [2] to demonstrate how to use model-based collaborative filtering method to build a recommender system in Python. Recommender systems are widely used in product recommendations such as recommendations of music, movies, books, news, research articles, restaurants, etc. [1][5]. The collaborative filtering method [5] predicts (filters) the interests of a user on a product by collecting preferences information from many other users (collaborating). The assumption behind the collaborative filtering method is that if a person P1 has the same opinion as another person P2 on an issue, P1 is more likely to share P2's opinion on a different issue than that of a randomly chosen person [5]. Content-based filtering method [6] utilizes product features/attributes to recommend other products similar to what the user likes, based on other users' previous actions or explicit feedback such as rating on products.

Nitin Rakesh, a distinguished leader in the IT services industry, is the Chief Executive Officer and Director of Mphasis. For centuries, banks demonstrated expertise in keeping, lending and saving money. In return, customers followed a bank's regulations. This included how banks stipulated interest rates for lending, identified creditworthy cohorts and facilitated banking transactions. Fast-forward to 2020, and banks are now viewed under the same lens as customer-facing organizations like movie theatres, restaurants and hotels.

The IQX platform is constantly improving, and our commitment to continuous improvement and alignment with our clients' strategic objectives has enabled us to become the global leader in AI-driven solutions for the group insurance industry today. Many of our latest enhancements and initiatives have already drawn praise from clients, and further establish our positioning on the leading edge of insurance innovation. To meet an increasing need for more streamlined underwriting and quoting, Global IQX is launching the most advanced recommendation and predictive analytics engine in the group insurance industry. Built using a range of complex AI and machine learning techniques, the new Recommendation Engine allows insurers to make quoting recommendations for both new and existing client renewals based on demographic similarities and other data points such as size and industry. Launching a Recommendation Engine as complex as this is an accomplishment in the insurance industry, where data is highly sensitive and not publicly available.

The next time you're wrapping up a trip (whenever that might be amid the COVID-19 pandemic), you might find yourself asking Google Assistant to check you out of a hotel. Google is bringing hands-free hospitality to hotel rooms through its Nest Hub smart displays. Participating hotels will set up a Nest Hub in each room, through which you can ask the front desk for a wake up call or more towels, and learn about pool opening times. They can set up the system to control smart devices in rooms, including blinds, lights and the TV. The Nest Hub can act as a concierge of sorts, as you can ask for recommendations and opening hours for restaurants and stores.

Summarization of speech is a difficult problem due to the spontaneity of the flow, disfluencies, and other issues that are not usually encountered in written texts. Our work presents the first application of the BERTSum model to conversational language. We generate abstractive summaries of narrated instructional videos across a wide variety of topics, from gardening and cooking to software configuration and sports. In order to enrich the vocabulary, we use transfer learning and pretrain the model on a few large cross-domain datasets in both written and spoken English. We also do preprocessing of transcripts to restore sentence segmentation and punctuation in the output of an ASR system. The results are evaluated with ROUGE and Content-F1 scoring for the How2 and WikiHow datasets. We engage human judges to score a set of summaries randomly selected from a dataset curated from HowTo100M and YouTube. Based on blind evaluation, we achieve a level of textual fluency and utility close to that of summaries written by human content creators. The model beats current SOTA when applied to WikiHow articles that vary widely in style and topic, while showing no performance regression on the canonical CNN/DailyMail dataset. Due to the high generalizability of the model across different styles and domains, it has great potential to improve accessibility and discoverability of internet content. We envision this integrated as a feature in intelligent virtual assistants, enabling them to summarize both written and spoken instructional content upon request.