We introduce an approach to recommending short-lived dynamic packages for golf booking services. Two challenges are addressed in this work. The first is the short life of the items, which puts the system in a state of a permanent cold start. The second is the uninformative nature of the package attributes, which makes clustering or figuring latent packages challenging. Although such settings are fairly pervasive, they have not been studied in traditional recommendation research, and there is thus a call for original approaches for recommender systems. In this paper, we introduce a hybrid method that leverages user analysis and its relation to the packages, as well as package pricing and environmental analysis, and traditional collaborative filtering. The proposed approach achieved appreciable improvement in precision compared with baselines.

COVID-19 has undoubtedly accelerated the application of AI in the healthcare industry, such as virus surveillance, diagnosis, and patient risk assessments. AI-powered robots and digital assistants with real-time monitoring and analysis have enabled doctors to provide more effective and personalized treatment. Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. It gives algorithms the ability to "learn" from training data so as to identify patterns and make decisions with little human intervention.

Group recommender systems facilitate group decision making for a set of individuals (e.g., a group of friends, a team, a corporation, etc.). Many of these systems, however, either assume that (i) user preferences can be elicited (or inferred) and then aggregated into group preferences or (ii) group preferences are partially observed/elicited. We focus on making recommendations for a new group of users whose preferences are unknown, but we are given the decisions/choices of other groups. By formulating this problem as group recommendation from group implicit feedback, we focus on two of its practical instances: group decision prediction and reverse social choice. Given a set of groups and their observed decisions, group decision prediction intends to predict the decision of a new group of users, whereas reverse social choice aims to infer the preferences of those users involved in observed group decisions. These two problems are of interest to not only group recommendation, but also to personal privacy when the users intend to conceal their personal preferences but have participated in group decisions. To tackle these two problems, we propose and study DeepGroup -- a deep learning approach for group recommendation with group implicit data. We empirically assess the predictive power of DeepGroup on various real-world datasets, group conditions (e.g., homophily or heterophily), and group decision (or voting) rules. Our extensive experiments not only demonstrate the efficacy of DeepGroup, but also shed light on the privacy-leakage concerns of some decision making processes.

Singles looking for love now have the option to declare their vaccination status. San Francisco-based dating app Coffee Meets Bagel is adding a Vaccine Status to its dating profile this week allowing singles to declare if they've received the COVID-19 vaccination, the company announced Thursday. Coffee Meets Bagel has a new vaccination feature on its app that lets users declare where they are in the process. Daters will be able to select from one of five responses to add to their dating profile: fully vaccinated, waiting on an additional dose, planning to get vaccinated, not getting vaccinated or prefer not to say, the company said. Users can edit responses at any time depending on their status.

For financial institutions that have yet to adopt conversational banking, the potential user base already exists. Nearly two-in-five U.S. adults are now users of smart speakers, such as Amazon Alexa or Google Home. In November 2020, eMarketer estimated that 128.0 million people in the US used a voice assistant – 44.2% of internet users and 38.5% of the total population. Consumers are programming themselves to find things using their voices. They're doing less button-pressing and more speaking to control their entertainment experiences, for example--from Amazon Fire TV to Apple TV.

I live in the curious intersection of art, design, and code. For the past two years, I've worked with a small group of artists to develop Alexa, Call Mom!, an immersive storytelling installation using Amazon's Alexa platform. Our project is far from the type of third-party apps you typically see for Amazon's voice assistant -- "Alexa, Play Jeopardy!" and "Alexa, Ask Pikachu to Talk" are two popular examples -- as it invites users to engage with Alexa in a way that's just a bit… off. Alexa, Call Mom! leads participants through an immersive séance experience. It is a parodic reimaging of the classic horror séance and an exploration of the tense relationships we share with conversational devices in our home.

The brushless motor that almost silently spins its 10.1-inch HD display around a 350-degree arc is the feature that will grab your attention when you take it out of the box, but you'll quickly discover many more things to get jazzed over when you set about exploiting its capabilities to the fullest. This is a fantastic feature whether you're following a recipe, engaging in a video call, or watching a movie on Netflix. And Amazon gives you full control over how motion occurs: You can disable it entirely, enable it only for some activities--such as when making video calls, watching a video, or following a recipe--or you can activate/deactivate it on demand by saying things like "Alexa, follow me," "Alexa, turn right," or "Alexa, turn off motion." If you place the Echo Show next to a wall or in a corner, you can adjust how far it will rotate so that it doesn't bump into anything as it spins. The display apparently has a clutch or a similar mechanism that automatically disengages the motor while at rest, allowing you to manually turn the display left or right even if motion is enabled.

Let's create a movie recommender based on ratings. In this example we have a collection of movies, a bunch of users, and movie ratings from users that range from 1 to 5. These ratings are sparse because each user rates only a small percentage of the total movies, and they are biased because users' ratings are distributed differently. Our goal is to take any user ID and search for recommended movies for that user. We will use Pinecone to tie everything together and expose the recommender as a real-time service that will take any user ID and return relevant movie recommendations.

Researchers at the University of Washington have figured out a way to use machine-learning algorithms to turn smart speakers into sensitive medical devices that can detect irregular heartbeats. The scientists use smart speakers like Amazon Echo or Google Home to send out an inaudible sound that bounces off a person's chest and returns to the device, reshaped in a way that reveals the heartbeat. An uneven cardiac rhythm can be associated with ailments including strokes or sleep apnea. The researchers employed a machine-learning algorithm to tease out the heartbeats from other sounds and signals such as breathing, which is easier to detect because it involves a much larger motion. The algorithm was also needed to zero in on erratic heart rhythms -- which from a health perspective are generally more important to identify than a steady "lub-dub."

You might soon have a new use for your Amazon Echo, Google Home or other smart speaker: checking your heart for irregular rhythms. Researchers at the University of Washington have developed an artificial intelligence system using smart speakers to monitor your heartbeat without requiring physical contact. Their findings were published in the peer-reviewed journal Communications Biology. The study had people sit 1 to 2 feet from a smart speaker, which starts playing an inaudible continuous sound. The sound then bounces off the person and back to the speaker, where the AI system is able to detect individual heartbeats.