Due to a large amount of information, it is difficult for users to find what they are interested in among the many choices. In order to improve users' experience, recommendation systems have been widely used in music recommendations, movie recommendations, online shopping, and other scenarios. Recently, Knowledge Graph (KG) has been proven to be an effective tool to improve the performance of recommendation systems. However, a huge challenge in applying knowledge graphs for recommendation is how to use knowledge graphs to obtain better user codes and item codes. In response to this problem, this research proposes a user Recurrent Neural Network (RNN) encoder and item encoder recommendation algorithm based on Knowledge Graph (URIR). This study encodes items by capturing high-level neighbor information to generate items' representation vectors and applies an RNN and items' representation vectors to encode users to generate users' representation vectors, and then perform inner product operation on users' representation vectors and items' representation vectors to get probabilities of users interaction with items. Numerical experiments on three real-world datasets demonstrate that URIR is superior performance to state-of-the-art algorithms in indicators such as AUC, Precision, Recall, and MRR. This implies that URIR can effectively use knowledge graph to obtain better user codes and item codes, thereby obtaining better recommendation results.

… Nettrice Gaskins—who teaches, writes, and makes art using algorithms and machine learning—spoke virtually on her work and creative process, …

Growing up as a child, we all at some point in time must have wanted to learn to play musical instruments, be it piano, violin, guitar, ukelele, drums, or saxophone. However, I was not good at playing any of the instruments, and playing a musical instrument remained a dream for me until now. So I decided to make an Artificial Intelligence model which could generate unique and unlimited music for me. Yes! Now, I would never get tired of hearing the same songs again. The Artificial Intelligence model would generate each time a unique and melodious song that I can listen to.

Why do we need this mathematics for ML and Data Science? Machine Learning is a subset of Artificial Intelligence (AI). Though AI and Data Science are two different fields, there are lots of things that overlap, between the two. We need to understand the mathematics that goes behind the models we use for AI and Data Science. The mathematics, we've learned Statistics and Probabilities are widely used in Data Science.

Over the past several decades, climate change has led to major disasters in Eastern Africa countries including Tanzania. From floods, chronic droughts, landslides, strong winds and earthquakes to their secondary impacts of diseases and epidemics, these are some of the recent disasters plaguing Tanzania. These disasters lead to death and displacement of people, loss of properties and livelihoods, disruption of social networks and services such as water, food, and healthcare thereby leaving communities more vulnerable and susceptible to the next extreme event. Lack of disaster preparedness and awareness makes the situation worse as communities remain helpless in the event of disasters hence face its full impact. Combining citizen science and modern technological innovation provides an opportunity to build the resilience of communities and reduce risks.

Employee employment is threatened by artificial intelligence. By signing this agreement and creating automated order kiosks at McDonald's restaurants, …

Among the most interesting features of the event is Adobe's continued integration of artificial intelligence into its products, a venue that the …

The feature, which airs Sunday night, is highlighted by Harari suggesting that the reach of artificial intelligence could leave our information in …

The company uses tech like big data analytics, artificial intelligence, and machine learning to create an interconnected network of stakeholders at …

This week's topics include artificial intelligence (AI) and bias; dietary calcium, protein, … Rick: And learning about machine learning.