A recommender system learns to predict the user-specific preference or intention over many items simultaneously for all users, making personalized recommendations based on a relatively small number of observations. One central issue is how to leverage three-way interactions, referred to as user-item-stage dependencies on a monotonic chain of events, to enhance the prediction accuracy. A monotonic chain of events occurs, for instance, in an article sharing dataset, where a ``follow'' action implies a ``like'' action, which in turn implies a ``view'' action. In this article, we develop a multistage recommender system utilizing a two-level monotonic property characterizing a monotonic chain of events for personalized prediction. Particularly, we derive a large-margin classifier based on a nonnegative additive latent factor model in the presence of a high percentage of missing observations, particularly between stages, reducing the number of model parameters for personalized prediction while guaranteeing prediction consistency. On this ground, we derive a regularized cost function to learn user-specific behaviors at different stages, linking decision functions to numerical and categorical covariates to model user-item-stage interactions. Computationally, we derive an algorithm based on blockwise coordinate descent. Theoretically, we show that the two-level monotonic property enhances the accuracy of learning as compared to a standard method treating each stage individually and an ordinal method utilizing only one-level monotonicity. Finally, the proposed method compares favorably with existing methods in simulations and an article sharing dataset.

Learn more about what comes next. Kore.ai, a no-code automation platform designed for enterprise applications, today announced that it raised $50 million in a series C round led by Vistara Growth and PNC with participation from Next Equity Partners, Nicola Wealth, and Beedie Capital, along with $20 million in debt from Sterling National Bank. The funds, which bring Kore's total raised to over $100 million to date, will be put toward expanding the company's workforce while developing new product features, according to cofounder and CEO Raj Koneru. In 2015, just 10% of organizations reported that they either already used automation technology or would be doing so in the near future. Fast forward to 2019, and that number rose to 37% -- which means that more than one in three organizations are either using AI or have plans to do so.

Has it really been 10 years of Siri? The Apple voice assistant was originally integrated into the iPhone 4S way back in October 2011, and we're now here to wish Siri a very happy 10th birthday. Sparking a trend for smart voice assistants across the board, Siri certainly changed how we all interact with technology these days, with the rise of Alexa no doubt helped substantially by the presence of Siri before it. It's possible that some of you won't remember the early beginnings of Siri – which is why we've taken a walk down memory lane and looked at the history behind how Siri came to be. We've also looked at just what it was like to use back in those early days, and considered what the next 10 years could mean for the (mostly) helpful voice assistant.

According to a survey conducted by PwC, 72% of business leaders use AI for their business advantage. The Digital marketing world has been restructured immensely since the emergence of AI. It helps companies develop powerful digital strategies, optimizes campaigns, and improves return on investment. Teleflora, a floral company in the US, used AI marketing to build new customers' profiles and improve customer loyalty. Using these historical data, Teleflora used AI marketing to predict the future customer behavior of different audience segments.

Have you ever wondered how virtual assistants like Siri and Cortana work? How do they understand what you're saying? Well, part of the answer is natural language processing. This interesting field of artificial intelligence has led to some huge breakthroughs over the last few years, but how exactly does it work? Read on to learn more about natural language processing, how it works, and how it's being used to make our lives more convenient.

With the release of Apple iOS 15 and the upcoming Google Pixel 6 device with Google's Tensor chip, we will soon see a tight competition between two giants in the field of artificial intelligence. The more an interface leverages human conversation, the less users have to learn how to use it. This guideline will help you design a better AI assistant experience. What aspects to consider when working on UI design of AI assistant. How should AI assistants look like?

From a business impact standpoint, recommendation systems help companies to increase their ROI by personalizing the content, based on user preferences. Let's take a concrete example. When you watch a video on YouTube, and you see a list of videos to watch next, that list is being built by a recommendation system. Recommendation engines are not just about suggesting products to users, they can also suggest users to products. Generally speaking, keep in mind that recommendation systems are not only about products that can be bought, as shown by Facebook friends and Instagram posts suggestions, based on recommendation systems.

The 2021 Apple TV 4K saw major improvements over the previous model, especially with performance and the improved Siri remote. If you've been waiting for a sale to pick one up, you can now save $20 on the 32GB model and buy one for just $159 at Adorama -- one of the better prices we've seen. With a review score of 90, the 2021 Apple TV 4K is among the best high-end streaming boxes available, particularly for Apple users. The A12 Bionic processor delivers zippy performance, and it supports Dolby Vision at 60 fps and Dolby Atmos sound, along with AirPlay 3 and screen mirroring. It also supports HomeKit, letting you ask Siri to show you video feeds, control smart lights, locks and more. The key difference with the last model, however, is the redesigned Siri remote.

The company announced today that it's making the Echo Show 10 devices in the US compatible with the popular video calling software. Users who have their calendars linked up to the Alexa will have their meetings started automatically while people who haven't done that can say, "Alexa, join my meeting" or "Alexa, join my Zoom meeting" to join one. This is the second Echo Show to gain Zoom access; the Echo Show 8 started supporting the videoconferencing platform in the US in December. The Echo Show 10's camera tracks users as they move throughout a room, meaning callers can see the screen no matter where they sit or stand. Presumably, this functionality will work with Zoom, putting it on par with other competitor devices like the Facebook Portal and Google Nest.

Food-choices and eating-habits directly contribute to our long-term health. This makes the food recommender system a potential tool to address the global crisis of obesity and malnutrition. Over the past decade, artificial-intelligence and medical researchers became more invested in researching tools that can guide and help people make healthy and thoughtful decisions around food and diet. In many typical (Recommender System) RS domains, smart nudges have been proven effective in shaping users' consumption patterns. In recent years, knowledgeable nudging and incentifying choices started getting attention in the food domain as well. To develop smart nudging for promoting healthier food choices, we combined Machine Learning and RS technology with food-healthiness guidelines from recognized health organizations, such as the World Health Organization, Food Standards Agency, and the National Health Service United Kingdom. In this paper, we discuss our research on, persuasive visualization for making users aware of the healthiness of the recommended recipes. Here, we propose three novel nudging technology, the WHO-BubbleSlider, the FSA-ColorCoading, and the DRCI-MLCP, that encourage users to choose healthier recipes. We also propose a Topic Modeling based portion-size recommendation algorithm. To evaluate our proposed smart-nudges, we conducted an online user study with 96 participants and 92250 recipes. Results showed that, during the food decision-making process, appropriate healthiness cues make users more likely to click, browse, and choose healthier recipes over less healthy ones.