As the world moves towards greater automation, the need for intelligent algorithms to facilitate decision-making processes is becoming increasingly crucial. In this regard, contextual bandits have emerged as a powerful tool for addressing adaptive learning and decision-making challenges in various real-world applications. This blog post will delve into the theoretical underpinnings of contextual bandits, elucidating …
In recent years, sequential recommender systems have emerged as a popular approach for providing personalized recommendations to users. These systems take into account the order in which items are consumed by users, enabling them to identify long-term and short-term interests of users and predict their future preferences accurately. However, while sequential recommender systems have proven …
Quantile regression is an advanced statistical technique that has gained significant attention in finance due to its ability to estimate conditional quantiles. While linear regression is widely used to model the relationship between two variables, it assumes a linear relationship between the dependent variable and the predictors. However, this assumption may not hold in many …
Unveiling Asymmetric Quantile Regression in Finance Read More »
Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, and presentation of data. The Central Limit Theorem (CLT) is a fundamental concept in statistics that underpins many statistical methods and analyses. It is a powerful tool for analyzing large datasets and predicting outcomes based on limited information. The CLT states that …
Mortality forecasting is a critical component of actuarial science, public policy, and social sciences. It helps decision-makers understand and plan for the impact of changing mortality patterns on various aspects of society. One of the most influential methods for mortality forecasting is the Lee-Carter algorithm, developed by Ronald Lee and Lawrence Carter in 1992. The …
Understanding the Lee-Carter Algorithm for Mortality Forecasting Read More »
Introduction Frequent Pattern Mining, also known as Association Rule Mining, is a powerful analytical process that helps in discovering frequent patterns, associations, or causal structures from various databases such as relational and transactional databases. It plays a significant role in Market Basket Analysis, a data analysis technique used by retailers to identify patterns in customer …
FP-Growth vs. Apriori: A Comprehensive Comparison of Frequent Pattern Mining Algorithms Read More »
Introduction Recommender systems have revolutionized the way we discover new products, music, movies, and even potential romantic partners. They analyze users’ past interactions with items and provide personalized recommendations based on their preferences. However, traditional recommender systems face several challenges, such as sparsity, cold-start, and scalability. To overcome these limitations, researchers have been exploring the …
Transform Your Recommender System with Temporal Graph Neural Networks Read More »
Introduction The explosion of data in recent years has led to an ever-increasing need for effective recommender systems. These systems are designed to analyze user behavior and preferences and provide recommendations for products, services, or content that are likely to be of interest to them. One of the most promising approaches to building these systems …
Building Recommender Systems with Graph Generative Models Read More »
Introduction Financial forecasting is a critical component of any business’s financial planning. The accuracy of these forecasts is essential for the decision-making process in the financial industry. In recent years, there has been a significant increase in the use of machine learning algorithms to make financial predictions. One such technology is SENet, which is being …
Leveraging SENet for More Accurate Financial Forecasting Read More »
Introduction Machine learning algorithms have become increasingly popular in recent years as businesses and organizations look for ways to extract value from their data. However, getting accurate predictions from machine learning models is not always easy. One of the biggest challenges is reducing overfitting and bias in the models, which can lead to inaccurate predictions. …
Using Optimal Subsampling and Bootstrap in Machine Learning: Improving Model Accuracy Read More »
