TV customers today face many choices from many live channels and on-demand services. Providing a personalised experience that saves customers time when discovering content is essential for TV providers. However, a reliable understanding of their behaviour and preferences is key. When creating personalised recommendations for TV, the biggest challenge is understanding viewing behaviour within households when multiple people are watching. The objective is to detect and combine individual profiles to make better-personalised recommendations for group viewing. Our challenge is that we have little explicit information about who is watching the devices at any time (individuals or groups). Also, we do not have a way to combine more than one individual profile to make better recommendations for group viewing. We propose a novel framework using a Gaussian mixture model averaging to obtain point estimates for the number of household TV profiles and a Bayesian random walk model to introduce uncertainty. We applied our approach using data from real customers whose TV-watching data totalled approximately half a million observations. Our results indicate that combining our framework with the selected features provides a means to estimate the number of household TV profiles and their characteristics, including shifts over time and quantification of uncertainty.

As any adult who loves video games knows, there are simply too many of them – 19,000 games were released in 2024 on PC games storefront Steam alone, not counting all the playable delights on consoles and smartphones. Most of us have backlogs of unplayed classics that make us feel guilty about buying newer games. Finding things that are actually good, meanwhile, can feel totally impossible. At least 50% of the questions people send in for this newsletter are a variant of "Help, what should I play?" We do our best to help, but even though it's my job to know about games, I still don't have infinite time to play them.

A few years ago I wrote about how, when planning my wedding, I'd signaled to the Pinterest app that I was interested in hairstyles and tablescapes, and I was suddenly flooded with suggestions for more of the same. Which was all well and fine until--whoops--I canceled the wedding and it seemed Pinterest pins would haunt me until the end of days. All of social media wanted to recommend stuff that was no longer relevant, and the stench of this stale buffet of content lingered long after the non-event had ended. So in this new era of artificial intelligence--when machines can perceive and understand the world, when a chatbot presents itself as uncannily human, when trillion-dollar tech companies use powerful AI systems to boost their ad revenue--surely those recommendation engines are getting smarter, too. Recommendation engines are some of the earliest algorithms on the consumer web, and they use a variety of filtering techniques to try to surface the stuff you'll most likely want to interact with--and in many cases, buy--online.

Meta is revamping how Facebook recommends videos across Reels, Groups, and the main Facebook Feed, by using AI to power its video recommendation algorithm, Facebook head Tom Alison revealed on Wednesday. The world's largest social network has already switched Reels, its TikTok competitor, to the new engine, and plans to use it in all places within Facebook that show video -- the main Facebook feed and Groups -- as part of a "technology roadmap" through 2026, Alison said at a Morgan Stanley tech conference in San Francisco. Meta has made competing with TikTok a top priority ever since the app, which serves up vertical video clips and is known for its powerful recommendation engine that seems to know exactly what will keep users hooked, started exploding in popularity in the US in the last few years. When Facebook tested the new AI-powered recommendation engine with Reels, watch time went up by roughly 8 to 10 percent, Alison revealed. "So what that told us was this new model architecture is learning from the data much more efficiently than the previous generation," Alison said. "So that was like a good sign that says, OK, we're on the right track."

The COVID-19 pandemic has intensified the urgency for effective and accessible mental health interventions in people's daily lives. Mobile Health (mHealth) solutions, such as AI Chatbots and Mindfulness Apps, have gained traction as they expand beyond traditional clinical settings to support daily life. However, the effectiveness of current mHealth solutions is impeded by the lack of context-awareness, personalization, and modularity to foster their reusability. This paper introduces CAREForMe, a contextual multi-armed bandit (CMAB) recommendation framework for mental health. Designed with context-awareness, personalization, and modularity at its core, CAREForMe harnesses mobile sensing and integrates online learning algorithms with user clustering capability to deliver timely, personalized recommendations. With its modular design, CAREForMe serves as both a customizable recommendation framework to guide future research, and a collaborative platform to facilitate interdisciplinary contributions in mHealth research. We showcase CAREForMe's versatility through its implementation across various platforms (e.g., Discord, Telegram) and its customization to diverse recommendation features.

Recommendation engine suggest content, product or services to the user by using machine learning algorithm. This paper proposed a content-based recommendation engine for providing video suggestion to the user based on their previous interests and choices. We will use TF-IDF text vectorization method to determine the relevance of words in a document. Then we will find out the similarity between each content by calculating cosine similarity between them. Finally, engine will recommend videos to the users based on the obtained similarity score value. In addition, we will measure the engine's performance by computing precision, recall, and F1 core of the proposed system.

Hurricane Ian made its way across Florida in late September 2022, causing tens of billions in estimated insurance losses due to wind and flood damage. Now, half a year later after the disaster, homeowners are still picking up the pieces and rebuilding with the payouts that have been slowly coming out from insurance policies. However, many have had the unexpected shock to learn that flooding was not a part of their homeowners insurance. Here we explain natural disasters, AI and insurance risk assessment. This event and many like it are stark reminders to both individuals and businesses that checking in with their insurance company to review insurance policies is something that needs to happen regularly, not because something may have gone unnoticed, but because things change.

In today's world, data is the engine that powers every company. The potential benefits of the data are being pursued by many significant organizations from various industries. Thanks to the solutions that data scientists have offered, several economic sectors are undergoing a fundamental revolution. As tech behemoths like IKEA, Amazon, and Netflix already make use of all potential advantages, the application of data science in the retail industry has increased as well. In India, the retail industry is expected to reach a whooping height of US$ 2 trillion by the year 2032, according to a survey held by the Boston Consulting Group. There is too much potential for income and growth for retailers and consumer goods companies in particular, in this data-driven world than can be ignored.

Machine Learning Roadmap: Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and predictive maintenance. Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products.

Technological advancement coupled with Artificial intelligence (AI) has taken over industries in the 21st century. For example, non-Fungible Tokens (NFTs) have exploded in popularity in recent years as a means of buying, selling, and trading unique digital assets such as artwork, music, and videos. As NFTs continue to gain in popularity, the incorporation of Artificial Intelligence is reshaping the NFT industry. Artificial Intelligence technology enables artists, collectors, and investors to generate, authenticate, and monetize non-fungible tokens in novel ways. The process of creating and validating NFTs becomes more rapid, efficient, and secure with Artificial Intelligence.