Your shopping carts may be full of fixings for stuffing and cranberry sauce, but Black Friday started early at Sonos, so make space for some of our favorite speakers, soundbars, and subwoofers. The sale runs through November 28, and with our favorite Sonos deals, it's a great opportunity to build a surround sound setup. Be sure to check out our other Black Friday coverage, including Deals on Google Devices, Deals on Microsoft Hardware, REI's'Get Up Get Out' Sale, Early Black Friday Deals, Early Best Buy Deals, and our Black Friday Shopping Tips. Special offer for Gear readers: Get a 1-year subscription to WIRED for $5 ($25 off). This includes unlimited access to WIRED.com and our print magazine (if you'd like). Subscriptions help fund the work we do every day.

Dating apps have become the most common way for couples in Japan who wed this year to first meet, with about 1 in every 5, or 22.6%, of newlywed pairs finding love online, according to a recent survey by an insurance firm. Online encounters overtook couples who met at work or school, which both came in at 20.8%, and those brought together by introductions from friends and acquaintances at 9.4%. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this FAQ.

Introduce a use case of a collaborative (based) filtering based recommendation system via deep learning. This article will not use the mathematical terms of linear algebra or term knowledge, which are occasionally mentioned, and do not count these too much. A question is raised here, can we use a kind of supervised learning algorithm to predict the rating a user might give by a user-ID and a movie-ID, or a user-ID and a lecture-ID? We then compare the estimated rating or quantity of interest with a reasonable threshold to decide whether we should recommend the item to the user or not. Recall that the vector (in row-wise) X0 and X1 can be stretched by Beta vector (two factors) on their way vertically and horizontally.

Traditional recommender systems aim to estimate a user's rating to an item based on observed ratings from the population. As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a systematic bias in the estimation. Consequently, a new trend in recommender system research is to negate the influence of confounders from a causal perspective. Observing that confounders in recommendations are usually shared among items and are therefore multi-cause confounders, we model the recommendation as a multi-cause multi-outcome (MCMO) inference problem. Specifically, to remedy confounding bias, we estimate user-specific latent variables that render the item exposures independent Bernoulli trials. The generative distribution is parameterized by a DNN with factorized logistic likelihood and the intractable posteriors are estimated by variational inference. Controlling these factors as substitute confounders, under mild assumptions, can eliminate the bias incurred by multi-cause confounders. Furthermore, we show that MCMO modeling may lead to high variance due to scarce observations associated with the high-dimensional causal space. Fortunately, we theoretically demonstrate that introducing user features as pre-treatment variables can substantially improve sample efficiency and alleviate overfitting. Empirical studies on simulated and real-world datasets show that the proposed deep causal recommender shows more robustness to unobserved confounders than state-of-the-art causal recommenders. Codes and datasets are released at https://github.com/yaochenzhu/deep-deconf.

Getting into the dynamic field of data science requires you to catch up and build on the trends of the industry. Building your portfolio is the right direction for it and solving the existing problems that can orchestrate breakthroughs in the industry is the perfect path to take. Finding the right project that fits your knowledge, matches with requirements of the industry, and gives you real world practical experience is a decision-heavy task. We have compiled a list of trending data science projects that you can explore to help refine your resume and land a job of your choice in 2023! For natural language processing, this data science project involves determining whether the data inferred is positive, negative, or neutral.

There has been a growing interest in HCI to understand the specific technological needs of people with dementia and supporting them in self-managing daily activities. One of the most difficult challenges to address is supporting the fluctuating accessibility needs of people with dementia, which vary with the specific type of dementia and the progression of the condition. Researchers have identified auto-personalized interfaces, and more recently, Artificial Intelligence or AI-driven personalization as a potential solution to making commercial technology accessible in a scalable manner for users with fluctuating ability. However, there is a lack of understanding on the perceptions of people with dementia around AI as an aid to their everyday technology use and its role in their overall self-management systems, which include other non-AI technology, and human assistance. In this paper, we present future directions for the design of AI-based systems to personalize an interface for dementia-related changes in different types of memory, along with expectations for AI interactions with the user with dementia.

Incorporating knowledge graph as side information has become a new trend in recommendation systems. Recent studies regard items as entities of a knowledge graph and leverage graph neural networks to assist item encoding, yet by considering each relation type individually. However, relation types are often too many and sometimes one relation type involves too few entities. We argue that it is not efficient nor effective to use every relation type for item encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational Knowledge Graphs for Recommendation), which explicitly distinguish the influence of different relations for item representation learning. We first construct virtual relational graphs (VRKGs) by an unsupervised learning scheme. We also design a local weighted smoothing (LWS) mechanism for encoding nodes, which iteratively updates a node embedding only depending on the embedding of its own and its neighbors, but involve no additional training parameters. We also employ the LWS mechanism on a user-item bipartite graph for user representation learning, which utilizes encodings of items with relational knowledge to help training representations of users. Experiment results on two public datasets validate that our VRKG4Rec model outperforms the state-of-the-art methods. The implementations are available at https://github.com/lulu0913/VRKG4Rec.

There's another opportunity to pick up Amazon's Echo speakers at their lowest prices to date as part of the company's early Black Friday sales. In many cases, they've dropped back down to the prices we saw during the fall edition of Prime Day. For one thing, the regular Echo is $50, which is half off the usual price. The latest Echo Dot is down from $50 to $25, and the fifth-gen Echo Dot with clock is a third off at $40. The Echo Dot kids' version is down 50 percent to $30 as well.

The Monitor is a weekly column devoted to everything happening in the WIRED world of culture, from movies to memes, TV to Twitter. On Tuesday morning, Ruben Martinez Jr. was staring at his computer screen, calculating his chances. He was on a group chat trying to strategize the best way to score Taylor Swift tickets, and it was looking bleak. Everyone seemed to have 2,000-plus people ahead of them in line. Martinez, a software engineer at OkCupid, checked the browser developer tools to see if he could figure out his actual place in the queue. He thought he could find a percentage for how far back he was.

Feeld Notes is a column about a middle-aged woman who suddenly realizes she wants to have sex again--and the beguiling app she uses to do it. The first man I had sex with in the decade since my divorce was not so much a man as, well, a boy. He was 29 years old, with a lean torso, olive-brown skin, and dark hair and eyes. He was more than 20 years younger than me. His name was Enrique, and like many of us on the app where we met, he looked different in his photographs than he did in real life.