In modern recommender systems, both users and items are associated with rich side information, which can help understand users and items. Such information is typically heterogeneous and can be roughly categorized into flat and hierarchical side information. While side information has been proved to be valuable, the majority of existing systems have exploited either only flat side information or only hierarchical side information due to the challenges brought by the heterogeneity. In this paper, we investigate the problem of exploiting heterogeneous side information for recommendations. Specifically, we propose a novel framework jointly captures flat and hierarchical side information with mathematical coherence. We demonstrate the effectiveness of the proposed framework via extensive experiments on various real-world datasets. Empirical results show that our approach is able to lead a significant performance gain over the state-of-the-art methods.

We explore the task of local music recommendation: provide listeners with personalized playlists of relevant tracks by artists who play most of their live events within a small geographic area. Most local artists tend to be obscure, long-tail artists and generally have little or no available user preference data associated with them. This creates a cold-start problem for collaborative filtering-based recommendation algorithms that depend on large amounts of such information to make accurate recommendations. In this paper, we compare the performance of three standard recommender system algorithms (Item-Item Neighborhood (IIN), Alternating Least Squares for Implicit Feedback (ALS), and Bayesian Personalized Ranking (BPR)) on the task of local music recommendation using the Million Playlist Dataset. To do this, we modify the standard evaluation procedure such that the algorithms only rank tracks by local artists for each of the eight different cities. Despite the fact that techniques based on matrix factorization (ALS, BPR) typically perform best on large recommendation tasks, we find that the neighborhood-based approach (IIN) performs best for long-tail local music recommendation.

Among the machine learning applications to business, recommender systems would take one of the top places when it comes to success and adoption. They help the user in accelerating the process of search while helping businesses maximize sales. Post phenomenal success in computer vision and speech recognition, deep learning methods are beginning to get applied to recommender systems. Current survey papers on deep learning in recommender systems provide a historical overview and taxonomy of recommender systems based on type. Our paper addresses the gaps of providing a taxonomy of deep learning approaches to address recommender systems problems in the areas of cold start and candidate generation in recommender systems. We outline different challenges in recommender systems into those related to the recommendations themselves (include relevance, speed, accuracy and scalability), those related to the nature of the data (cold start problem, imbalance and sparsity) and candidate generation. We then provide a taxonomy of deep learning techniques to address these challenges. Deep learning techniques are mapped to the different challenges in recommender systems providing an overview of how deep learning techniques can be used to address them. We contribute a taxonomy of deep learning techniques to address the cold start and candidate generation problems in recommender systems. Cold Start is addressed through additional features (for audio, images, text) and by learning hidden user and item representations. Candidate generation has been addressed by separate networks, RNNs, autoencoders and hybrid methods. We also summarize the advantages and limitations of these techniques while outlining areas for future research.

With the abundance of data in recent years, interesting challenges are posed in the area of recommender systems. Producing high quality recommendations with scalability and performance is the need of the hour. Singular Value Decomposition(SVD) based recommendation algorithms have been leveraged to produce better results. In this paper, we extend the SVD technique further for scalability and performance in the context of 1) multi-threading 2) multiple computational units (with the use of Graphical Processing Units) and 3) distributed computation. We propose block based matrix factorization (BMF) paired with SVD. This enabled us to take advantage of SVD over basic matrix factorization(MF) while taking advantage of parallelism and scalability through BMF. We used Compute Unified Device Architecture (CUDA) platform and related hardware for leveraging Graphical Processing Unit (GPU) along with block based SVD to demonstrate the advantages in terms of performance and memory.

You can save even more on the Ring Video Doorbell 2 when you order via Amazon Alexa. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. Our picks and opinions are independent from USA TODAY's newsroom and any business incentives. Round two of Amazon Prime Day 2019 is here and there are still plenty of amazing discounts to be had. Chances are you've probably spent time perusing a plethora of deals on Amazon, but have you tried using Alexa to save big?

The UK's National Health Service (NHS) has announced what it claims is a world first: a partnership with Amazon's Alexa to offer health advice from the NHS website. Britons who ask Alexa basic health questions like "Alexa, how do I treat a migraine?" The partnership does not add significantly to Alexa's skill-set, but it is an interesting step for the NHS. The UK's Department of Health (DoH) says it hopes the move will reduce the pressure on health professionals in the country, giving people a new way to access reliable medical advice. It will also benefit individuals with disabilities, like sight impairments, who may find it difficult to use computers or smartphones to find the same information.

Digital assistants have become a major trend in government at every level and across geographies, and could soon be a mainstay in many state and federal agencies in the U.S. Recent favorable signs include an executive order launching the American AI Initiative and the Health and Human Services Department awarding 57 spots on its Intelligent Automation/Artificial Intelligence (AI) contract, according to natural language processing (NLP) expert William Meisel, president of TMA Associates. Speaking at the AI World Government conference, held last month in Washington, D.C., Meisel says digital assistants (aka "intelligent" or "virtual" assistants) are among the most developed and least risky ways to implement AI--and "the closest to what we see in sci-fi." Digital assistants are broadly applicable across departments and agencies looking to cut costs and boost human productivity and have a minimum probability of failure and unintended consequences. For a citizenry looking for answers, they're also a "nice alternative to automated systems and long hold times," he adds. Juniper Research reports that, by 2023, one-quarter of the populace will be using digital voice assistants daily, says Meisel.

Want to keep an eye out for porch pirates? Or perhaps you're seeking a simpler way to see who is at your front door without having to get off the couch? Smart doorbells are still fairly new to the world of home security, and the market is becoming more and more saturated with options by the minute. This begs the question: How do you know which one is right for you and your home? I've been living with the Ring Video Doorbell Pro for about a year and a half now.

Perhaps you've heard the recent rumblings that Google is revamping its smart home line to exclusively include Nest products--and is rebranding under the name Google Nest. If not, here's a brief rundown of what happened: Google made the big announcement on May 7, indicating that Google Nest would be eliminating the Works with Nest program that allows Nest products to be controlled by smart assistants like Amazon Alexa. Since then, I've been wondering what exactly that means for how I'll control my Nest products going forward. I have a Nest thermostat and several Nest outdoor cameras and I often use Amazon Alexa (and the Nest app) to control these smart home devices. Other Nest users shared in my bewilderment, taking to Twitter to express their confusion about the merger.

Intelligent Personal Assistants (IPAs) have become widely popular in recent times. Most of the commercial IPAs today support a wide range of skills including Alarms, Reminders, Weather Updates, Music, News, Factual Questioning-Answering, etc. The list grows every day, making it difficult to remember the command structures needed to execute various tasks. An IPA must have the ability to communicate information about supported skills and direct users towards the right commands needed to execute them. Users interact with personal assistants in natural language. A query is defined to be a Help Query if it seeks information about a personal assistant's capabilities, or asks for instructions to execute a task. In this paper, we propose an interactive system which identifies help queries and retrieves appropriate responses. Our system comprises of a C-BiLSTM based classifier, which is a fusion of Convolutional Neural Networks (CNN) and Bidirectional LSTM (BiLSTM) architectures, to detect help queries and a semantic Approximate Nearest Neighbours (ANN) module to map the query to an appropriate predefined response. Evaluation of our system on real-world queries from a commercial IPA and a detailed comparison with popular traditional machine learning and deep learning based models reveal that our system outperforms other approaches and returns relevant responses for help queries.