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.

Amazon has kicked off another sale on its Echo Show smart devices ahead of Black Friday. Among the discounts are the Echo Show 5 down to $35, the Echo Show 8 down to $70, and both the Echo Show 10 and Echo Show 15 each down to $170. The Kids edition of the Echo Show 5 is also on sale for $40. We've seen all of these deals at various points in the past, but each match the lowest prices we've tracked to date, including the discounts we saw during the company's Prime Early Access Sale in October. There's a chance things drop lower on Black Friday proper, but since Amazon is advertising these offers as "early Black Friday deals," we'd expect them to carry over.

Detroit carjackings are up 40 percent compared to last year - and the ages of the kids doing them – have become unbelievable. A Tennessee man stands accused of carjacking his Tinder date at gunpoint last year and trying to sell her car back to her for $500. Elijah Darius Scott, 25, of Memphis, Tennessee, landed in the Shelby County Jail on Tuesday after being charged with carjacking and aggravated robbery, as well as employment of a firearm during a dangerous felony, according to local outlet WREG. The alleged victim informed officers that after agreeing to meet a man she knew only as Darius, Scott entered the passenger side of the vehicle, placed a gun next to her and demanded her phone and money while threatening to shoot her. Elijah Darius Scott, 25, allegedly carjacked a Memphis woman and offered to sell her car back to her for $500.

Best AI Technology leaders are Google, Amazon, IBM, Microsoft, Salesforce, Oracle, NVIDIA, Intel, SAP, and Adobe. The global artificial intelligence market is expected to grow from $2.9 billion in 2019 to $19.6 billion by 2024, at a CAGR of 42.6% during the forecast period. The growth of the artificial intelligence market is driven by the increasing demand for intelligent virtual assistants, such as Amazon Alexa and Google Home, and the increasing adoption of AI-based technologies by enterprises. According to Zion Market Research, The global artificial intelligence market is anticipated to increase from $59.7 billion in 2021 to $422.4 billion by 2028. Robotics, automation, and AI are causing disruption in almost every business.

Modern recommender systems face an increasing need to explain their recommendations. Despite considerable progress in this area, evaluating the quality of explanations remains a significant challenge for researchers and practitioners. Prior work mainly conducts human study to evaluate explanation quality, which is usually expensive, time-consuming, and prone to human bias. In this paper, we propose an offline evaluation method that can be computed without human involvement. To evaluate an explanation, our method quantifies its counterfactual impact on the recommendation. To validate the effectiveness of our method, we carry out an online user study. We show that, compared to conventional methods, our method can produce evaluation scores more correlated with the real human judgments, and therefore can serve as a better proxy for human evaluation. In addition, we show that explanations with high evaluation scores are considered better by humans. Our findings highlight the promising direction of using the counterfactual approach as one possible way to evaluate recommendation explanations.

We introduce Cohort Comfort Models, a new framework for predicting how new occupants would perceive their thermal environment. Cohort Comfort Models leverage historical data collected from a sample population, who have some underlying preference similarity, to predict thermal preference responses of new occupants. Our framework is capable of exploiting available background information such as physical characteristics and one-time on-boarding surveys (satisfaction with life scale, highly sensitive person scale, the Big Five personality traits) from the new occupant as well as physiological and environmental sensor measurements paired with thermal preference responses. We implemented our framework in two publicly available datasets containing longitudinal data from 55 people, comprising more than 6,000 individual thermal comfort surveys. We observed that, a Cohort Comfort Model that uses background information provided very little change in thermal preference prediction performance but uses none historical data. On the other hand, for half and one third of each dataset occupant population, using Cohort Comfort Models, with less historical data from target occupants, Cohort Comfort Models increased their thermal preference prediction by 8~\% and 5~\% on average, and up to 36~\% and 46~\% for some occupants, when compared to general-purpose models trained on the whole population of occupants. The framework is presented in a data and site agnostic manner, with its different components easily tailored to the data availability of the occupants and the buildings. Cohort Comfort Models can be an important step towards personalization without the need of developing a personalized model for each new occupant.

It's the go-to dating app for many people looking for love, and now Hinge has introduced a new feature called'Relationship Type.' The tool allows users to specify the type of commitment they're looking for, whether it's monogamous or polyamorous. Michelle Parsons, Chief Product Officer at Hinge, said: 'With the launch of Relationship Type, we are empowering users to openly share what kind of relationship they are looking for, and as a result, have a new way to know if someone's dating goals match theirs from the moment they look at their profile.' It's the go-to dating app for many people looking for love, and now Hinge has introduced a new feature called'Relationship Type' The tool allows users to specify the type of commitment they're looking for, whether it's monogamous or polyamorous Most singletons want monogamous relationships, in which they only have one partner. However, research has shown that about five per cent of relationships are openly non-monogamous, or polyamorous.

SHOPPING: Products featured in this article are independently selected by our shopping writers. If you make a purchase using links on this page, MailOnline will earn an affiliate commission. If you've been holding off investing in the best smart home devices as they tend to be expensive, Amazon has dropped tons of epic deals ahead of Black Friday, including 64 per cent off this smart home bundle. Amazon has blessed us with early Black Friday deals, so you don't have to wait until the shopping bonanza officially starts on Friday, 25 November, to shop and save on gadgets to help automate your home, and you don't have to be a Prime member. This discounted bundle deal on not one but two Amazon Echo Dot (3rd Gen) devices and a Meross Smart Plug - everything you need to create an Alexa smart home routine.

An A.I.-powered online fashion marketplace, Shoptrue, is launching its website into beta today with plans for a public release early next year. The site blends artificial intelligence and personalized recommendations with taste-driven shopping, the company says, which helps give users a source for style inspiration as well as the ability to create and share outfit ideas with others. Rather than the typical algorithmic approach such as Amazon, which ranks items based on a strong sales history, Shoptrue is A.I.-driven and continually improves its product recommendations based on purchase behaviors and user engagement. That way, users can have more say on what items they see on their curated feeds. The site offers a "One Stop Personal Shop" for the user, which gives fashion suggestions based on their style preferences.

Due to the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the ranking-based group identification (RGI) task, i.e., recommending groups to users. The major challenge in this task is how to effectively and efficiently leverage both the item interaction and group participation of users' online behaviors. Though recent developments of Graph Neural Networks (GNNs) succeed in simultaneously aggregating both social and user-item interaction, they however fail to comprehensively resolve this RGI task. In this paper, we propose a novel GNN-based framework named Contextualized Factorized Attention for Group identification (CFAG). We devise tripartite graph convolution layers to aggregate information from different types of neighborhoods among users, groups, and items. To cope with the data sparsity issue, we devise a novel propagation augmentation (PA) layer, which is based on our proposed factorized attention mechanism. PA layers efficiently learn the relatedness of non-neighbor nodes to improve the information propagation to users. Experimental results on three benchmark datasets verify the superiority of CFAG. Additional detailed investigations are conducted to demonstrate the effectiveness of the proposed framework.