We investigate how well traditional fiction genres like Fantasy, Thriller, and Literature represent readers' preferences. Using user data from Goodreads we construct a book network where two books are strongly linked if the same people tend to read or enjoy them both. We then partition this network into communities of similar books and assign each a list of subjects from The Open Library to serve as a proxy for traditional genres. Our analysis reveals that the network communities correspond to existing combinations of traditional genres, but that the exact communities differ depending on whether we consider books that people read or books that people enjoy. In addition, we apply principal component analysis to the data and find that the variance in the book communities is best explained by two factors: the maturity/childishness and realism/fantastical nature of the books. We propose using this maturity-realism plane as a coarse classification tool for stories.

You probably want a streaming device(Opens in a new tab) for your TV, whether you're a cord cutter(Opens in a new tab) or not. Roku is a popular choice, particularly as it ramps up its own original content(Opens in a new tab). Roku devices offer plenty of variety and portability, from the budget Roku Express(Opens in a new tab) to the feature-packed Roku Ultra(Opens in a new tab). Whichever one you have, there's more to know beyond the basics. Here's how to get more out of your streaming device.

An Arlo doorbell will work with Alexa, the Google Assistant, Siri, Samsung's SmartThings or IFTTT integrations. Unlike some smart home devices, Arlo plays nice. And right now, you can save $100 on the brand's wire-free version of the Essential Video Doorbell at Amazon. You can also get the same discount through Arlo's site directly. Get half off a no-wires-required video doorbell from a brand that works with whichever smart home assistant you prefer. The rechargeable battery inside makes the unit easy to install, particularly if your front entry isn't already wired for a doorbell.

It's the go-to dating app for many singletons, but it appears that Hinge is experiencing issues this afternoon. According to Down Detector, the problems started at around midday and are affecting users across the country. While the reason for the outage remains unclear, of those who reported issues, 99 per cent said they were struggling with the app, while the remaining one per cent said they were having problems logging in. One user posted a screenshot of the error message in the app, which reads: 'We'll be right back. Something went wrong, and we're working on fetching solutions.

NEEDHAM, Mass., March 7, 2023 – A new forecast from the International Data Corporation (IDC) Worldwide Artificial Intelligence Spending Guide shows that global spending on artificial intelligence (AI), including software, hardware, and services for AI-centric systems*, will reach $154 billion in 2023, an increase of 26.9% over the amount spent in 2022. The ongoing incorporation of AI into a wide range of products will result in a compound annual growth rate (CAGR) of 27.0% over the 2022-2026 forecast with spending on AI-centric systems expected to surpass $300 billion in 2026. "Companies that are slow to adopt AI will be left behind – large and small. AI is best used in these companies to augment human abilities, automate repetitive tasks, provide personalized recommendations, and make data-driven decisions with speed and accuracy," said Mike Glennon, senior market research analyst with IDC's Customer Insights & Analysis team. "Suppliers of AI technologies need to know which are the largest and fastest growing opportunities, but without data they become just another opinion. IDC's AI Spending Guide provides the foundation for marketing strategy through its comprehensive coverage of AI opportunities and gives a robust basis for a market focus that ties with companies' capabilities."

We address the issue of binary classification from positive and unlabeled data (PU classification) with a selection bias in the positive data. During the observation process, (i) a sample is exposed to a user, (ii) the user then returns the label for the exposed sample, and (iii) we however can only observe the positive samples. Therefore, the positive labels that we observe are a combination of both the exposure and the labeling, which creates a selection bias problem for the observed positive samples. This scenario represents a conceptual framework for many practical applications, such as recommender systems, which we refer to as ``learning from positive, unlabeled, and exposure data'' (PUE classification). To tackle this problem, we initially assume access to data with exposure labels. Then, we propose a method to identify the function of interest using a strong ignorability assumption and develop an ``Automatic Debiased PUE'' (ADPUE) learning method. This algorithm directly debiases the selection bias without requiring intermediate estimates, such as the propensity score, which is necessary for other learning methods. Through experiments, we demonstrate that our approach outperforms traditional PU learning methods on various semi-synthetic datasets.

Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models only collecting the intermediate parameters instead of the real user data, which greatly enhances the user privacy. Beside, federated recommendation systems enable to collaborate with other data platforms to improve recommended model performance while meeting the regulation and privacy constraints. However, federated recommendation systems faces many new challenges such as privacy, security, heterogeneity and communication costs. While significant research has been conducted in these areas, gaps in the surveying literature still exist. In this survey, we-(1) summarize some common privacy mechanisms used in federated recommendation systems and discuss the advantages and limitations of each mechanism; (2) review some robust aggregation strategies and several novel attacks against security; (3) summarize some approaches to address heterogeneity and communication costs problems; (4)introduce some open source platforms that can be used to build federated recommendation systems; (5) present some prospective research directions in the future. This survey can guide researchers and practitioners understand the research progress in these areas.

Fashion is an ever-evolving industry that requires constant adaptation and innovation to stay relevant. One of the latest technological advancements in the industry is the use of deep learning algorithms for fashion recommendation systems. In this blog, we will explore how to use the ResNet50 model for building a fashion recommendation system. For this point of time, we create one streamlit webpage on localsystem to see the 10 recommended fashion product images which looks similar to query image. The ResNet50 is a deep convolutional neural network that was introduced by Microsoft Research in 2015.

Artificial Intelligence is on everyone's lips. Everywhere you turn from ChatGPT to bots to Alexa and Siri personal assistants, everyone is talking about AI. Well, with so much demand for AI, you would need some competent people to create and manage these services. Hence why I am writing my 5 reasons you should take the AI-900 Azure AI Fundamentals exam now!

Machine Learning (ML) is a branch of Artificial Intelligence (AI) that uses data and algorithms to imitate how humans learn. It is used in different fields like Ecommerce, Healthcare, Manufacturing, Aerospace, Banking, Finance & Insurance. We are categorizing our emails, using virtual personal assistants, getting product recommendations. But would you let an AI diagnose you? Would you be able to trust an AI more than a doctor?