Online healthcare communities provide users with various healthcare interventions to promote healthy behavior and improve adherence. When faced with too many intervention choices, however, individuals may find it difficult to decide which option to take, especially when they lack the experience or knowledge to evaluate different options. The choice overload issue may negatively affect users' engagement in health management. In this study, we take a design-science perspective to propose a recommendation framework that helps users to select healthcare interventions. Taking into account that users' health behaviors can be highly dynamic and diverse, we propose a multi-armed bandit (MAB)-driven recommendation framework, which enables us to adaptively learn users' preference variations while promoting recommendation diversity in the meantime. To better adapt an MAB to the healthcare context, we synthesize two innovative model components based on prominent health theories. The first component is a deep-learning-based feature engineering procedure, which is designed to learn crucial recommendation contexts in regard to users' sequential health histories, health-management experiences, preferences, and intrinsic attributes of healthcare interventions. The second component is a diversity constraint, which structurally diversifies recommendations in different dimensions to provide users with well-rounded support. We apply our approach to an online weight management context and evaluate it rigorously through a series of experiments. Our results demonstrate that each of the design components is effective and that our recommendation design outperforms a wide range of state-of-the-art recommendation systems. Our study contributes to the research on the application of business intelligence and has implications for multiple stakeholders, including online healthcare platforms, policymakers, and users.

Wink's switch to a subscription service for its smart home platform hasn't had the best start. The company has confirmed reports (via Android Police and The Verge) of a wide-ranging outage affecting its services, including integration with Amazon Alexa and Google Home/Nest services and even Wink.com The problems appear to have started on September 10th. The company's status page indicated that fixed had been put in place for at least some issues. However, there were still "partial" outages listed for most support as of this writing (September 12th), and "degraded performance" for both the Core and Hub frameworks.

Apple will stage its traditional post-Labor Day product reveal on Tuesday, where it is expected to tout new editions of the Apple Watch and iPad. Along the way, there will be new things for Siri to do as well on the iPad, as part of the iOS mobile operating system upgrade. Siri is the oft-maligned but heavily used personal assistant. This year, Siri will tout a "completely new look," with "over 20x more facts than just three years ago." Yes, Apple actually says this, on the promo page for the iOS 14 upgrade, which has traditionally been made available in September.

The Flo by Moen Smart Water Leak Detector is a reliable gadget to help protect your home from water damage. Flo by Moen's Smart Water Leak Detector is the very best smart water leak detector we've ever tested, beating out our previous No. 1 pick, the Honeywell Home Water Leak Detector. While the two detectors are comparable, Moen is the more affordable of the two and, most of all, we were impressed by the near-instantaneous alerts (an imperative function of a smart leak detector) the Moen leak sensor sent to both of our iOS and Android devices, as well as via email. The Flo by Moen Smart Water Leak Detector passed all of our tests with flying colors and even continued to function after being submerged in water during our final round of testing. When a leak is detected, the sensor begins playing an alarm sound and flashes red, in addition to sending timely alerts in a matter of seconds. However, what it lacks in voice-control capabilities, it makes up for with a beautifully designed app full of useful data insights. The sensor also keeps track of the temperature and humidity within your home, which can help with moisture control.

Labor Day weekend deals extended into the rest of the week with a number of sales on electronics and home appliances. Amazon's $99 Instant Pot deal is still available while August's latest WiFi smart lock can still be had for $30 off. Some of the latest gadgets are on sale, too, including Apple's 2020 iPad Pros and Samsung's Galaxy Note 20 Ultra smartphone (which just came out). Here are the best deals from this week that you can still grab today. The Galaxy Note 20 Ultra has only been available for about a month and it's already on sale.

This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision. Also stochastic Hopfield network with hidden units. Also exhaustive search or generate and test. Also deep structured learning or hierarchical learning.

Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the "music mainstream" strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervised learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user's country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-a-vis state-of-the-art algorithms that do not exploit this type of context information.

With children talking to smart-speakers, smart-phones and even smart-microwaves daily, it is increasingly important to educate students on how these agents work-from underlying mechanisms to societal implications. Researchers are developing tools and curriculum to teach K-12 students broadly about artificial intelligence (AI); however, few studies have evaluated these tools with respect to AI-specific learning outcomes, and even fewer have addressed student learning about AI-based conversational agents. We evaluate our Conversational Agent Interface for MIT App Inventor and workshop curriculum with respect to eight AI competencies from the literature. Furthermore, we analyze teacher (n=9) and student (n=47) feedback from workshops with the interface and recommend that future work leverages design considerations from the literature to optimize engagement, collaborates with teachers, and addresses a range of student abilities through pacing and opportunities for extension. We found students struggled most with the concepts of AI ethics and learning, and recommend emphasizing these topics when teaching. The appendix, including a demo video, can be found here: https://gist.github.com/jessvb/1cd959e32415a6ad4389761c49b54bbf

Game recommendation is an important application of recommender systems. Recommendations are made possible by data sets of historical player and game interactions, and sometimes the data sets include features that describe games or players. Collaborative filtering has been found to be the most accurate predictor of past interactions. However, it can only be applied to predict new interactions for those games and players where a significant number of past interactions are present. In other words, predictions for completely new games and players is not possible. In this paper, we use a survey data set of game likes to present content based interaction models that generalize into new games, new players, and both new games and players simultaneously. We find that the models outperform collaborative filtering in these tasks, which makes them useful for real world game recommendation. The content models also provide interpretations of why certain games are liked by certain players for game analytics purposes.

Beyond asking for the latest temperature, calendar appointments and recipes, Amazon Echo and Google Nest Hub devices can be used for phone calls. Amazon announced on Wednesday a new alliance with wireless carrier AT&T to enable AT&T customers (on "eligible rate plans") to link their mobile numbers and turn their speaker into a two-way phone. This will enable them to make calls and answer their phone from contacts at home by saying "Alexa answer" without having to search for the phone, or answer on a dead battery. You can also have a choice of where you want to answer, via the phone, on your device, or Echo speaker. The alliance is exclusive with AT&T.