Research about recommender systems emerges over the last decade and comprises valuable services to increase different companies' revenue. Several approaches exist in handling paper recommender systems. While most existing recommender systems rely either on a content-based approach or a collaborative approach, there are hybrid approaches that can improve recommendation accuracy using a combination of both approaches. Even though many algorithms are proposed using such methods, it is still necessary for further improvement. In this paper, we propose a recommender system method using a graph-based model associated with the similarity of users' ratings, in combination with users' demographic and location information. By utilizing the advantages of Autoencoder feature extraction, we extract new features based on all combined attributes. Using the new set of features for clustering users, our proposed approach (GHRS) has gained a significant improvement, which dominates other methods' performance in the cold-start problem. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms many existing recommendation algorithms on recommendation accuracy.

Artificial intelligence (AI) is everywhere: personal digital assistants answer our questions, robo-advisors trade stocks for us, and driverless cars will someday take us where we want to go. AI has penetrated our lives, and its use is exploding in biomedical research and health care--including across all dimensions of cancer research, where the potential applications for AI are vast. Artificial Intelligence (AI) is a computer performing tasks commonly associated with human intelligence. Humans are coding or programing a computer to act, reason, and learn. An algorithm or model is the code that tells the computer how to act, reason, and learn.

Artificial intelligence (AI) is the use of "computers and machines to mimic the problem-solving and decision-making capabilities of the human mind," according to IBM. [1] The idea of AI goes back at least 2,700 years. As Adrienne Mayor, research scholar, folklorist, and science historian at Stanford University, explained: "Our ability to imagine artificial intelligence goes back to the ancient times. Long before technological advances made self-moving devices possible, ideas about creating artificial life and robots were explored in ancient myths." Mayor noted that the myths about Hephaestus, the Greek god of invention and blacksmithing, included precursors to AI. For example, Hephaestus created the giant bronze man, Talos, which had a mysterious life force from the gods called ichor.

The World Economic Forum's The Future of Jobs Report 2020 estimates that 85 million jobs could be "displaced" by AI before 2025. Whether you treat the technology with suspicion or embrace it wholeheartedly, one thing's for certain: it's here to stay. "We do predict a change in many, if not most, jobs with the adoption of AI," says Naeema Pasha, co-author of Futureproof Your Career. "We'll see more of it at work. There'll be more use of its facial-recognition capabilities, for instance, while conversation tools such as Amazon's Alexa will move beyond our kitchens into our workplaces. As such, more and more roles that we might identify as'administrative' will become AI-based."

We present a novel recommender systems dataset that records the sequential interactions between users and an online marketplace. The users are sequentially presented with both recommendations and search results in the form of ranked lists of items, called slates, from the marketplace. The dataset includes the presented slates at each round, whether the user clicked on any of these items and which item the user clicked on. Although the usage of exposure data in recommender systems is growing, to our knowledge there is no open large-scale recommender systems dataset that includes the slates of items presented to the users at each interaction. As a result, most articles on recommender systems do not utilize this exposure information. Instead, the proposed models only depend on the user's click responses, and assume that the user is exposed to all the items in the item universe at each step, often called uniform candidate sampling. This is an incomplete assumption, as it takes into account items the user might not have been exposed to. This way items might be incorrectly considered as not of interest to the user. Taking into account the actually shown slates allows the models to use a more natural likelihood, based on the click probability given the exposure set of items, as is prevalent in the bandit and reinforcement learning literature. \cite{Eide2021DynamicSampling} shows that likelihoods based on uniform candidate sampling (and similar assumptions) are implicitly assuming that the platform only shows the most relevant items to the user. This causes the recommender system to implicitly reinforce feedback loops and to be biased towards previously exposed items to the user.

Machine learning on source code (MLOnCode) is a popular research field that has been driven by the availability of large-scale code repositories and the development of powerful probabilistic and deep learning models for mining source code. Code-to-code recommendation is a task in MLOnCode that aims to recommend relevant, diverse and concise code snippets that usefully extend the code currently being written by a developer in their development environment (IDE). Code-to-code recommendation engines hold the promise of increasing developer productivity by reducing context switching from the IDE and increasing code-reuse. Existing code-to-code recommendation engines do not scale gracefully to large codebases, exhibiting a linear growth in query time as the code repository increases in size. In addition, existing code-to-code recommendation engines fail to account for the global statistics of code repositories in the ranking function, such as the distribution of code snippet lengths, leading to sub-optimal retrieval results. We address both of these weaknesses with \emph{Senatus}, a new code-to-code recommendation engine. At the core of Senatus is \emph{De-Skew} LSH a new locality sensitive hashing (LSH) algorithm that indexes the data for fast (sub-linear time) retrieval while also counteracting the skewness in the snippet length distribution using novel abstract syntax tree-based feature scoring and selection algorithms. We evaluate Senatus via automatic evaluation and with an expert developer user study and find the recommendations to be of higher quality than competing baselines, while achieving faster search. For example, on the CodeSearchNet dataset we show that Senatus improves performance by 6.7\% F1 and query time 16x is faster compared to Facebook Aroma on the task of code-to-code recommendation.

Every month, Amazon pushes a slate of updates to its Alexa-enabled devices. One of the more noteworthy features Amazon added this month is the ability to move music between Echo devices using your voice. If you want to do so between different speakers in your home, say "Alexa, pause" to the one currently playing music, and then say "Alexa, resume music here" to the device where you want to move your tunes to. The feature also works with Echo Buds and Echo Auto, allowing you to take your music on the go. If you're a football fan with an Echo Show, another new feature allows you to ask Alexa to play the Two-Minute drill, an NFL pregame show that will offer expert analysis on the next match your favorite team is about to play.

Products featured in this Mail Best article are independently selected by our shopping writers. If you make a purchase using links on this page, MailOnline may earn an affiliate commission. To celebrate Amazon Alexa's Birthday, you can currently shop popular Echo devices on sale for as little as £24.99 - so now is the perfect time to get your hands on that gadget you've been eyeing up. Ahead of Black Friday 2021, which officially kicks off on November 27, while these offers last, shoppers can get Amazon Echo Dot smart speakers and Echo Show smart displays with up to 40 per cent off. To celebrate Amazon Alexa's Birthday, you can currently shop popular Echo devices on sale for as little as £24.99

Alexa's robotic voice sometimes feels a bit annoying but do you know that you can easily change its voice to different celebrity voices? The best thing is you don't need to go through various steps and processes just simple command and you will be able to enjoy your favorite celebrity voice. However you need to know one more things that this facility won't be free and you have to spend $4.99. The voices are quite limited though. In 2019 Amazon announced their first and new celebrity voice of Samuel L Jackson for Alexa.

Maternal and child mortality is a public health problem that disproportionately affects low- and middle-income countries. Every day, 800 women and 6,700 newborns die from complications related to pregnancy or childbirth. And for every maternal death, about 20 women suffer serious birth injuries. However, nearly all of these deaths and negative health outcomes are preventable. Midwives are key to revert this situation, and thus it is essential to strengthen their capacities and the quality of their education. This is the aim of the Safe Delivery App, a digital job aid and learning tool to enhance the knowledge, confidence and skills of health practitioners. Here, we use the behavioral logs of the App to implement a recommendation system that presents each midwife with suitable contents to continue gaining expertise. We focus on predicting the click-through rate, the probability that a given user will click on a recommended content. We evaluate four deep learning models and show that all of them produce highly accurate predictions.