Recently, graph neural networks (GNNs) have been widely used to develop successful recommender systems. Although powerful, it is very difficult for a GNN-based recommender system to attach tangible explanations of why a specific item ends up in the list of suggestions for a given user. Indeed, explaining GNN-based recommendations is unique, and existing GNN explanation methods are inappropriate for two reasons. First, traditional GNN explanation methods are designed for node, edge, or graph classification tasks rather than ranking, as in recommender systems. Second, standard machine learning explanations are usually intended to support skilled decision-makers. Instead, recommendations are designed for any end-user, and thus their explanations should be provided in user-understandable ways. In this work, we propose GREASE, a novel method for explaining the suggestions provided by any black-box GNN-based recommender system. Specifically, GREASE first trains a surrogate model on a target user-item pair and its $l$-hop neighborhood. Then, it generates both factual and counterfactual explanations by finding optimal adjacency matrix perturbations to capture the sufficient and necessary conditions for an item to be recommended, respectively. Experimental results conducted on real-world datasets demonstrate that GREASE can generate concise and effective explanations for popular GNN-based recommender models.

Dating app Tinder has a message for the metaverse: it's not you, it's me. The company is reducing its commitment to moving into the much-touted virtual reality realm as it reels from an operating loss of $10 million in the most recent financial quarter. In February, 2021 Match Group bought South Korean company Hyperconnect for over $1.7 billion. At the time, top executives hyped the purchase as one that would see Match Group's various dating apps slide into DMs of the future metaverse thanks to Hyperconnect's live video and chat technologies. The metaverse, which has been highly pushed by Meta CEO Mark Zuckerberg and other Silicon Valley moguls, can include virtual reality and also augmented reality that would combine aspects of the physical and digital worlds.

The arrests were made after someone complained that he had been blackmailed - police say the fraudsters threatened to publish nude photos of him on social media unless he paid them some money.

Don't expect to find a Tinder date in the metaverse any time soon. The Verge reports Match Group chief Bernard Kim has asked Tinder's Hyperconnect unit (acquired in 2021) to scale back its metaverse dating plans. In his shareholder letter, Kim said "uncertainty" about success with virtual worlds required that the team "not invest heavily" in the metaverse. Match further blamed the Hyperconnect purchase for a $10 million operating loss in the latest quarter where it made a $210 million operating profit in the same period a year earlier. The company is also taking "a step back" on plans to introduce its in-app Tinder Coins following questionable test results, Kim said.

The chief executive of Tinder has left the dating app after less than a year after the market value of its parent company plunged by more than a fifth following reporting disappointing results. The departure of Renate Nyborg was one of a number of management changes announced by the $20bn Match Group, which owns dating brands including Hinge, Tinder and Match.com. Its share price plunged by more than 20% on Tuesday after missing Wall Street second-quarter expectations and issuing weaker-than-expected guidance. "Today we are announcing the departure of Tinder chief executive Renate Nyborg, and I have made some changes to the management team and structure that I am confident will help deliver Tinder's full potential," said Bernard Kim, the chief executive of Match Group, in a letter to shareholders. "We have not been able to realise the monetisation successes that we typically deliver. Tinder's current revenue growth expectations for the second half of the year are below our original expectations as a result of disappointing execution on several optimisations and new product initiatives."

"Today we're announcing the departure of Tinder CEO Renate Nyborg, and I have made some changes to the management team and structure that I am confident will help deliver Tinder's full potential," Match Group chief executive Bernard Kim said in a letter to shareholders.

Recently, graph neural networks have shown the superiority of modeling the complex topological structures in heterogeneous network-based recommender systems. Due to the diverse interactions among nodes and abundant semantics emerging from diverse types of nodes and edges, there is a bursting research interest in learning expressive node representations in multiplex heterogeneous networks. One of the most important tasks in recommender systems is to predict the potential connection between two nodes under a specific edge type (i.e., relationship). Although existing studies utilize explicit metapaths to aggregate neighbors, practically they only consider intra-relationship metapaths and thus fail to leverage the potential uplift by inter-relationship information. Moreover, it is not always straightforward to exploit inter-relationship metapaths comprehensively under diverse relationships, especially with the increasing number of node and edge types. In addition, contributions of different relationships between two nodes are difficult to measure. To address the challenges, we propose HybridGNN, an end-to-end GNN model with hybrid aggregation flows and hierarchical attentions to fully utilize the heterogeneity in the multiplex scenarios. Specifically, HybridGNN applies a randomized inter-relationship exploration module to exploit the multiplexity property among different relationships. Then, our model leverages hybrid aggregation flows under intra-relationship metapaths and randomized exploration to learn the rich semantics. To explore the importance of different aggregation flow and take advantage of the multiplexity property, we bring forward a novel hierarchical attention module which leverages both metapath-level attention and relationship-level attention. Extensive experimental results suggest that HybridGNN achieves the best performance compared to several state-of-the-art baselines.

Amazon Prime Day is now over - for July, anyway. According to leaked notices there may be another Prime Day deals extravaganza in Q4 later this year, as we reported earlier, but for now there are still plenty of discounts on everything from TVs to toys, and we've rounded up the best ones here. Jump to section: 1. Today's best deals 2. Amazon device deals 3. Back to school deals 4. TV deals 5. Laptop deals 6. Every choice on our list has been picked out by our expert editorial team here at TechRadar and we stand by our recommendations. We're well aware that everyone is feeling the pressure this year with inflation, gas prices, and many other household essentials becoming more expensive. We've made sure we've included bargains on a range of useful products that won't break the bank, as well as our old favorites like premium TVs. Where applicable, we'll always tell you if a Prime Day deal is at its lowest ever price or not too. Now that the main Amazon Prime Day 2022 sale is over, you no longer need to be a member to take advantage of today's offers. If you no longer want or need to keep your subscription going, make sure you find out how to cancel Amazon Prime while it's ongoing so you aren't charged for another month. With all that covered, let's head on into TechRadar's official list of the best Amazon Prime Day deals of 2022 that you can still buy. Apple Watch SE (40mm, GPS): $279 $219 at Amazon (opens in new tab) Save $70 - On a budget? We highly recommend the Apple Watch SE as the best smartwatch (opens in new tab) as the affordable iOS option. While it doesn't have as big of a screen or some of the advanced ECG and blood oxygen monitoring as the Apple Watch 7, the SE is still a great choice. It still has all those fitness, health, and lifestyle apps that the Apple Watches are famed for. Waterpik Aquarius dental flosser: $99.99 $54.99 at Amazon (opens in new tab) Save $45 - Amazon is currently offering this Waterpik Aquarius dental flosser for just over half price. It was $10 cheaper on Prime Day itself, but a 40% discount still isn't bad. It comes with 10 customization settings, a built-in timer to help you track flossing time and even a massage mode for gum stimulation.

Artificial Intelligence (AI) and machine learning are now accessible to WordPress users through AI-powered plugins. Currently, 77% of consumers use AI technology services or products, whether they are aware of it or not. It includes intelligent WordPress plugins. AI applications help boost businesses running on WordPress, the most popular Content Management System (CMS) platform powering over 35% of all websites to date. Initially a blogging platform, WordPress has become an extensive CMS, compared to Drupal or Joomla, and other similar platforms.

The past few years have seen a massive boom in smartphone technology and applications, mainly due to the rapid evolution in artificial intelligence and related technologies. AI has managed to completely transform the user interface and experience for even the simplest mobile apps that we use on a daily basis. In today's world, half of our daily tasks are automated and performed by mobile apps that are powered by AI, and it is truly a magical time to be alive. Speaking of artificial intelligence, it is impertinent to mention that the ever-growing phenomenon continues to morph mobile apps into something entirely different. From physical learning to AI-based learning, from physical piano lessons to online lessons for piano – everything is dominated by AI.