In this paper, we consider recommender systems with side information in the form of graphs. Existing collaborative filtering algorithms mainly utilize only immediate neighborhood information and have a hard time taking advantage of deeper neighborhoods beyond 1-2 hops. The main caveat of exploiting deeper graph information is the rapidly growing time and space complexity when incorporating information from these neighborhoods. In this paper, we propose using Graph DNA, a novel Deep Neighborhood Aware graph encoding algorithm, for exploiting deeper neighborhood information. DNA encoding computes approximate deep neighborhood information in linear time using Bloom filters, a space-efficient probabilistic data structure and results in a per-node encoding that is logarithmic in the number of nodes in the graph. It can be used in conjunction with both feature-based and graph-regularization-based collaborative filtering algorithms. Graph DNA has the advantages of being memory and time efficient and providing additional regularization when compared to directly using higher order graph information. We conduct experiments on real-world datasets, showing graph DNA can be easily used with 4 popular collaborative filtering algorithms and consistently leads to a performance boost with little computational and memory overhead.

Not all voice assistants can handle the same requests. We put Siri, Alexa and Google to the test. LOS ANGELES – When you pose the simplest of questions to Siri and it can't answer "what's 1% of $1 million," yet Amazon Alexa and the Google Assistant can, you know just how far behind Apple's assistant has fallen to rivals. Siri was the first voice assistant, announced in 2011 for the iPhone 4S, but over the past several years, Amazon and Google have rolled over it, by investing heavily and introducing many new features, while Siri "hasn't moved forward much," says Bret Kinsella, who runs the Voicebot.ai Apple traditionally takes center stage at its Worldwide Developer's Conference (WWDC) to unveil new software features to whet app makers' appetites, and often they involve Siri.

Alexa's poor reputation for privacy may soon worsen as a patent filed by the firm suggests the virtual assistant may start listening before its'wake word' is said. Under the plans Alexa will be able to detect when it is being given a command even if the wake word is said at the end of the sentence instead of at the front. The move raises concerns over user privacy as Alexa will, by default, always be listening to conversations on the off-chance its wakeword is spoken. Alexa's poor reputation for privacy may soon worsen as a patent filed by the firm suggests the virtual assistant may start listening before its'wake word' is said. The patent, filed with the US Patent and Trademark Office, reveals the Seattle-fimrs plans for the next evolutionary step for it Alexa's technology.

The U.S. and China are locked in an increasingly heated struggle for superpower status. Many perceived this confrontation initially only through the lenses of a trade war. However, the ZTE "saga" already indicated the issue was broader and involved a battle for supremacy over 21st century technologies and, relatedly, for international power (see When AI Started Creating AI – Artificial Intelligence and Computing Power, 7 May 2018). The technological battle increasingly looks like a fight to the death, with the offensive against Huawei, aiming notably to protect future 5G networks (Cassell Bryan-Low, Colin Packham, David Lague, Steve Stecklow And Jack Stubbs, "The China Challenge: the 5G Fight", Reuters Investigates, 21 May 2019). For Huawei and China, as well as for the world, consequences are far reaching, as, after Google "stopping Huawei's Android license", and an Intel and Qualcomm ban, the British chip designer ARM, held notably by Japanese Softbank, now stops relations with Huawei (Paul Sandle, "ARM supply halt deals fresh blow to Chinese tech giant Huawei", Reuters, 22 May 2019; "DealBook Briefing: The Huawei Backlash Goes Global", The New York Times, 23 May 2019; Tom Warren, "Huawei's Android And Windows Alternatives Are Destined For Failure", The Verge, 23 May 2019). The highly possible coming American move against Chinese Hikvision, one of the largest world producers of video surveillance systems involving notably "artificial intelligence, speech monitoring and genetic testing" would only further confirm the American offensive (Doina Chiacu, Stella Qi, "Trump says'dangerous' Huawei could be included in U.S.-China trade deal", Reuters, 23 May 2019; Ana Swanson and Edward Wong, "Trump Administration Could Blacklist China's Hikvision, a Surveillance Firm", The New York Times, 21 May 2019). China, for its part, answers to both the trade war and the technological fight with an ideologically martial mobilisation of its population along the lines of "People's War", "The Long March", and changing TV scheduling to broadcast war films (Iris Zhao and Alan Weedon, "Chinese television suddenly switches scheduling to anti-American films amid US-China trade war", ABC News, 20 May 2019; Michael Martina, David Lawder, "Prepare for difficult times, China's Xi urges as trade war simmers", Reuters, 22 May 2019). This highlights how much is as stake for the Middle Kingdom, as we explained previously ( Sensor and Actuator (4): Artificial Intelligence, the Long March towards Advanced Robots and Geopolitics).

Did you know that Artificial Intelligence will contribute a whopping $15.7 trillion to the global economy by 2030!? In addition to economic benefits, AI is also responsible for making our lives simpler. This article on Benefits Of Artificial Intelligence will help you understand how Artificial Intelligence is impacting all domains of our life and at last benefiting humankind. I'll be discussing the benefits of Artificial Intelligence in the following domains: To get in-depth knowledge of Artificial Intelligence & Machine Learning, you can enroll for live Machine Learning Engineer Master Program by Edureka with 24/7 support and lifetime access. Artificial Intelligence can be used to automate anything ranging from tasks that involve extreme labor to the process of recruitment.

GSMA Director General Mats Granryd outlines 5G's brisk growth since the beginning of 2018, and shares his excitement about how the combination of intelligent connectivity will create smarter applications that make life better and safer. I ntelligent connectivity enables transformational capabilities in transport, entertainment, industry, and much more. For technical systems to digitally match human actions with connected environments, however, they must meet the speed of our natural reaction times. They will also rely on cost-effective edge infrastructure to enable scaling. According to GSMA, 5G could account for as many as 1.4 billion connections by 2025.

When you talk about Artificial Intelligence there are many changes that has been grown and implemented over different platforms and specially over eCommerce platform. The technology has been attracting everyone to get more innovative into the online market. Artificial Intelligence in eCommerce is revolutionizing by shaping the world of online shopping experience by creating new standards. It is been done to understand the customer by fulfilling their better experience and satisfying expectations through this new eCommerce artificial intelligence techniques. Below you will find now new opportunities to implement on your online business. Embed this infographic on your site The growth of Artificial Intelligence is currently rising very speedily and this is what creates new eCommerce world to take steps toward future with new technologies.

With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where user-item interactions are sparse. To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed. Since these models use auxiliary attributes, they are effective in cold start settings. However, most of the proposed models are either unable to capture complex feature interactions or not properly designed to combine user-item feedback information with content information. In this paper, we propose Self-Attentive Integration Network (SAIN) which is a model that effectively combines user-item feedback information and auxiliary information for recommendation task. In SAIN, a self-attention mechanism is used in the feature-level interaction layer to effectively consider interactions between multiple features, while the information integration layer adaptively combines content and feedback information. The experimental results on two public datasets show that our model outperforms the state-of-the-art models by 2.13%

We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN encoder-decoders combined with intermediate supervision to improve the final prediction performance. Unlike the graph convolutional matrix completion model with one-hot encoding node inputs, our STAR-GCN learns low-dimensional user and item latent factors as the input to restrain the model space complexity. Moreover, our STAR-GCN can produce node embeddings for new nodes by reconstructing masked input node embeddings, which essentially tackles the cold start problem. Furthermore, we discover a label leakage issue when training GCN-based models for link prediction tasks and propose a training strategy to avoid the issue. Empirical results on multiple rating prediction benchmarks demonstrate our model achieves state-of-the-art performance in four out of five real-world datasets and significant improvements in predicting ratings in the cold start scenario. The code implementation is available in https://github.com/jennyzhang0215/STAR-GCN.

It's never easy to predict the future, and the speakers of Data Science Salon New York have somewhat divergent views on how we might expect ML and AI to be applied to the field of Media and Entertainment in the next five to ten years. But there's one clear area of agreement: "We're going to continue to see the implementation (and improvement) of personalized recommendation algorithms that are based on person-level data." said Lauren Lombardo, Senior Data Scientist at Nielsen. "It will vastly improve the overall relevance of the content and advertisements served across premium video, and make content exploration much simpler and more enjoyable," suggests Chris Whitely, Senior Director, Applied Analytics at Comcast. But the recommendation engines of tomorrow will be even more robust. "We've barely scratched the surface of utilizing recommendation engines for generating new content," said Josh Miller, Director of Data Analytics at Samba.tv.