Due to the shallow structure, classic graph neural networks (GNNs) failed in modelling high-order graph structures that deliver critical insights of task relevant relations. The negligence of those insights lead to insufficient distillation of collaborative signals in recommender systems. In this paper, we propose PEAGNN, a unified GNN framework tailored for recommendation tasks, which is capable of exploiting the rich semantics in metapaths. PEAGNN trains multilayer GNNs to perform metapath-aware information aggregation on collaborative subgraphs, $h$-hop subgraphs around the target user-item pairs. After the attentive fusion of aggregated information from different metapaths, a graph-level representation is then extracted for matching score prediction. To leverage the local structure of collaborative subgraphs, we present entity-awareness that regularizes node embedding with the presence of features in a contrastive manner. Moreover, PEAGNN is compatible with the mainstream GNN structures such as GCN, GAT and GraphSage. The empirical analysis on three public datasets demonstrate that our model outperforms or is at least on par with other competitive baselines. Further analysis indicates that trained PEAGNN automatically derives meaningful metapath combinations from the given metapaths.

Artificial intelligence (AI) is creeping into our everyday lives, often without us realizing it. Today, AI can be found in the digital assistants we use such as Apple's (NASDAQ:AAPL) Siri and Amazon's (NASDAQ:AMZN) Alexa to check our schedules and search for things on the internet; in the cars we own that now park themselves as they are able to recognize space around the vehicle; and in the small robots we use to clean our houses, such as the Roomba vacuum. Artificial intelligence is becoming more a part of our lives all the time, and will only grow in importance in coming years. In the not too distant future, AI will influence everything from how we shop for groceries to how diseases are diagnosed and treated by doctors. It all adds up to a fast growing market.

Following the launch of investigations last year, the U.S. Department of Justice (DOJ) together with attorney generals from 11 U.S. states filed a lawsuit against Google on Tuesday alleging that the company maintains monopolies in online search and advertising, and violates laws prohibiting anticompetitive business practices. It's the first antitrust lawsuit federal prosecutors filed against a tech company since the Department of Justice brought charges against Microsoft in the 1990s. "Back then, Google claimed Microsoft's practices were anticompetitive, and yet, now, Google deploys the same playbook to sustain its own monopolies," the complaint reads. "For the sake of American consumers, advertisers, and all companies now reliant on the internet economy, the time has come to stop Google's anticompetitive conduct and restore competition." Attorneys general from no Democratic states joined the suit.

Machine Learning is an algorithmic approach of creating computer models with the ability to learn and adapt from a given data-set, these models can then be used to make useful predictions of results against similar but never-seen-before data. It is often referred to as a subset of Artificial Intelligence and forms the very base on which AI models are created. The concept of Machine Learning is based on the idea that whether machines can be designed to imitate human behaviour of learning, adapting skills, and applying where necessary. Just like all living beings who learn from every experience in life and take future decisions, similarly, the Machine Learning approach creates models that are first trained to'learn' on a data-set distribution. The trained models predict results by applying the knowledge learned during training with reasonable high accuracy.

From instantaneous translation to conversational interfaces, artificial-intelligence (AI) technologies are making ever more evident impacts on our lives. This is particularly true in the financial-services sector, where challengers are already launching disruptive AI-powered innovations. To remain competitive, incumbent banks must become "AI first" in vision and execution, and as discussed in our previous article, 1 1. Suparna Biswas, Brant Carson, Violet Chung, Shwaitang Singh, and Renny Thomas, "AI-bank of the future: Can banks meet the AI challenge?," If fully integrated, these capabilities can strengthen engagement significantly, supporting customers' financial activities across diverse online and physical contexts with intelligent, highly personalized solutions delivered through an interface that is intuitive, seamless, and fast.

One of our favorite gadgets from 2019 was the Google-powered Lenovo Smart Clock. It doesn't have all the bells and whistles of a typical Google smart display, but its alarm clock features, affordable price point and small form factor more than make up for it. Recently, however, the company debuted an even simpler version of the device, appropriately called the Lenovo Smart Clock Essential. With the Essential, the pretense of a smart display is gone altogether; the LCD screen has been replaced with a basic LED display. As a result, I don't quite like it as much as the original Lenovo Smart Clock, but it's also $30 cheaper (the Essential retails for $50 while the original Smart Clock is $80) and if all you really want is an alarm clock with some Google Assistant smarts, then the Essential certainly fits the bill. At its core, the Lenovo Smart Clock Essential is simply a Google-powered smart speaker with a built-in alarm clock.

Amazon Prime Day was the unofficial start to the holiday shopping season, and if you're tired of scrolling your phone for things to buy, don't worry. You can get everything you need using Amazon Alexa voice shopping to buy the best gifts for everyone on your list. Alexa voice shopping is an amazing service that lets you find and buy products on Amazon's website without doing any real work. Here's how to use it. From a smart screen to a smartphone, there are plenty of ways you can get your holiday shopping done with the help of Amazon Alexa.

The revamped, fourth-generation Echo Dot has arrived, and it comes with a spherical design that sets it well apart from its smaller, flatter predecessors. But while it boasts an all-new look, the latest Echo Dot is essentially the same speaker as the (now steeply discounted) third-gen Dot, complete with onboard Alexa, stellar smart home capabilities, impressive communication features, and the ability to keep an ear on your home via Alexa Guard. And while we're pleased with how the new Dot sounds, its front-firing driver doesn't deliver a quantum leap over the older Dot's audio quality. So yes, the fourth-gen Echo Dot is technically the best Dot yet, but only by a narrow margin. Available in charcoal, glacier white, and twilight blue flavors, the fourth-gen Echo Dot marks a major departure in terms of the speaker's look and feel.

Users around the world can now use Google's Assistant to play and control Spotify podcasts in English, Spotify has confirmed to Engadget. Until now, the voice assistant has only supported its own Google Podcasts, with third-party services limited to music streaming only. However, recent rumors suggested that the third-party podcast support was coming, starting with Spotify. You can enable the feature by heading to your Assistant device's settings in the Google Home app (on a smartphone or tablet) and choosing Spotify as the default podcast provider. You'll be able to find more information here from Spotify once the news post goes live.

Remember Facebook's automated personal assistant, M, that was released in a bid to compete with Alexa and Siri? After a series of embarrassing mishaps due to poorly trained algorithms, Facebook abruptly pulled the plug. They weren't alone; chatbots are infamous for putting their metaphorical feet in their mouths. While these debacles are tough to watch, the underlying problem is not artificial intelligence (AI) itself. AI succeeds when underpinned with sound strategy and well-trained models.