Our network is trained using triplets of songs such that two songs by the same In this paper, we explore the feasibility of using Triplet artist are embedded closer to one another than to networks, a variant of Siamese networks (Bromley et al., a third song by a different artist. We compare 1994), for content-based music recommendation. In this two models that are trained using different ways context, a Triplet network learns an embedding of an item of picking this third song: at random vs. based such that the item is close to other similar items and far on shared genre labels. Our experiments are conducted from dissimilar items in the embedding space. To train using songs from the Free Music Archive the network, we will consider songs by the same artist to and use standard audio features. The initial results be similar and songs by all other artists to be dissimilar.

Any organization needs to improve their products, services, and processes. In this context, engaging with customers and understanding their journey is essential. Organizations have leveraged various techniques and technologies to support customer engagement, from call centres to chatbots and virtual agents. Recently, these systems have used Machine Learning (ML) and Natural Language Processing (NLP) to analyze large volumes of customer feedback and engagement data. The goal is to understand customers in context and provide meaningful answers across various channels. Despite multiple advances in Conversational Artificial Intelligence (AI) and Recommender Systems (RS), it is still challenging to understand the intent behind customer questions during the customer journey. To address this challenge, in this paper, we study and analyze the recent work in Conversational Recommender Systems (CRS) in general and, more specifically, in chatbot-based CRS. We introduce a pipeline to contextualize the input utterances in conversations. We then take the next step towards leveraging reverse feature engineering to link the contextualized input and learning model to support intent recognition. Since performance evaluation is achieved based on different ML models, we use transformer base models to evaluate the proposed approach using a labelled dialogue dataset (MSDialogue) of question-answering interactions between information seekers and answer providers.

The world of mobile apps is changing, and it's all thanks to artificial intelligence (AI). An increasing number of companies are investing in AI technology that can be used to improve their products and services. But why should you care? What types of impacts could this have on your business? Let's explore some ways that AI will impact the development process for mobile apps: AI-powered personal assistants are becoming increasingly popular, and they can help with a range of tasks.

Artificial intelligence (AI) has undoubtedly changed many aspects of our lives, from how we work, meet people, discover information and even the careers we choose. The rate of change in society spurred through the developments in AI will almost certainly accelerate as it becomes more accessible to companies and to the general public. Its usefulness and level of "intelligence" directly correlate to the processing power of computer processors. What does this mean for you as an investor? AI is the leading supercycle in the technology sector and will have a profound impact on society.

Learning to rank is an effective recommendation approach since its introduction around 2010. Famous algorithms such as Bayesian Personalized Ranking and Collaborative Less is More Filtering have left deep impact in both academia and industry. However, most learning to rank approaches focus on improving technical accuracy metrics such as AUC, MRR and NDCG. Other evaluation metrics of recommender systems like fairness have been largely overlooked until in recent years. In this paper, we propose a new learning to rank algorithm named Pareto Pairwise Ranking. We are inspired by the idea of Bayesian Personalized Ranking and power law distribution. We show that our algorithm is competitive with other algorithms when evaluated on technical accuracy metrics. What is more important, in our experiment section we demonstrate that Pareto Pairwise Ranking is the most fair algorithm in comparison with 9 other contemporary algorithms.

Matrix Factorization is one of the most successful recommender system techniques over the past decade. However, the classic probabilistic theory framework for matrix factorization is modeled using normal distributions. To find better probabilistic models, algorithms such as RankMat, ZeroMat and DotMat have been invented in recent years. In this paper, we model the user rating behavior in recommender system as a Poisson process, and design an algorithm that relies on no input data to solve the recommendation problem and the cold start issue at the same time. We prove the superiority of our algorithm in comparison with matrix factorization, random placement, Zipf placement, ZeroMat, DotMat, etc.

In our weekly recap on Sunday, we, as editors, look back at the past seven days. We do this at the suggestion of our cartoonist Albert Jan Rasker. He chooses a subject, makes a drawing, and we take it from there. If you'd like to receive this weekly recap directly in your mailbox every Sunday morning, just subscribe here. Artificial Intelligence (AI) has come to control our lives step by step.

Feeld Notes is a column about a middle-aged woman who suddenly realizes she wants to have sex again--and the beguiling app she uses to do it. Men are disappearing on me all the time on this fucking app. Within the first six weeks of my time on Feeld, I'd had some fun, but I'd also been flaked on or ghosted no fewer than six times. There was one guy, Mike, a short, brown-haired, 33-year-old writer who described himself as a "dom" and promised to show me the ropes around domination and submission. I spent a few steamy nights chatting with him on WhatsApp, then made a plan to meet up with him on a Friday night.

Artificial intelligence, or AI, is everywhere right now. In truth, the fundamentals of AI and machine learning have been around for a long time. The first primitive form of AI was an automated checkers bot which was created by Cristopher Strachey from the University of Manchester, England, back in 1951. It's come a long way since then, and we're starting to see a large number of high profile use cases for the technology being thrust into the mainstream. Some of the hottest applications of AI include the development of autonomous vehicles, facial recognition software, virtual assistants like Amazon's AMZN Alexa and Apple's AAPL Siri and a huge array of industrial applications in all industries from farming to gaming to healthcare.

Microsoft Cortana, we barely knew ye. A combination of inflationary effects of money printing through the pandemic, Russia's war of aggression in Ukraine, and even China's COVID-Zero lockdown policies -- are all contributing to a perfect economic storm hitting practically everyone. From gas prices to food costs, we're all tightening our belts, and for the world's biggest companies, that means accepting some harsh realities about some of their more experimental departments. Some of the biggest tech stories of this year revolved around Facebook's staggering fall from grace, with billions upon billions wiped off its market value owing to a smorgasbord of strategic and macroeconomic headwinds. The focus on its experimental metaverse saw the company rebrand to Meta and imagine a future where users sought to wear bulky VR computers on their faces to do basic tasks rather than the palm-sized pocket computers they already owned. Amazon, quite similarly, has come to terms with its own human-computer interfacing failure this year, although you probably wouldn't have realized it from how popular they are.