Interpretability Archives - Sinfa Consulting: Data Science & AI Consulting

Modeling Multiple User Interests using Hierarchical Knowledge for Conversational Recommender System

In today’s world, e-commerce has become an integral part of our lives, and the success of these platforms relies on providing personalized recommendations to users. Recommender systems, therefore, play a crucial role in providing these personalized recommendations, and are used extensively in various industries, including music, movies, and online shopping. Recommender systems utilize users’ past …

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Using Multivariate Time Series Forecasting with Transformers in Finance

In today’s fast-paced financial world, businesses require accurate and timely financial forecasting to make informed decisions. Multivariate time series forecasting has become a popular technique in finance to predict future values of multiple variables simultaneously. It is a statistical method that models the behavior of several interdependent variables over time, considering the historical data of …

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Transform Your Recommender System with Temporal Graph Neural Networks

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 …

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Building Recommender Systems with Graph Generative Models

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 …

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The Future of Social Network Analysis: GCNs and Beyond

The field of social network analysis has come a long way in recent years, with new techniques and technologies emerging to help researchers and practitioners gain deeper insights into the complex relationships and interactions within social networks. One of the most promising developments in this area is the use of graph convolutional networks (GCNs), which …

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Penalized Two-Pass Regression: A Step-by-Step Guide

Introduction Penalized regression is a technique used in machine learning and statistics to improve the performance of linear regression models. One specific variation of penalized regression is known as two-pass regression, which involves two stages of variable selection and regularization. In this blog post, we will discuss the concept of penalized two-pass regression, its advantages, …

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