Statistical methods have been used in the time series domain for multiple decades. But given the recent advances in Machine Learning and especially its sub-domain Deep Learning, are statistical methods still superior for forecasting? In this article, we will do a deep dive into literature and recent time series competitions to do a multifaceted comparison between Statistical, Machine Learning, and Deep Learning methods for time series forecasting.

IntroductionLarge-scale information retrieval applications, such as recommender systems, search ranking, and text analysis often leverage feature interactions for effective modeling. These models are commonly deployed at the ranking stage of the cascade-style systems. In this article, I summarize the need for modeling feature interactions and introduce some of the most popular ML architectures designed around this theme. This article also highlights the high data sparsity issue, that makes it hard for ML algorithms to model second or higher-order feature interactions.

This article explains what explicit and implicit feedback data means for recommender systems. We discuss their characteristics and peculiarities concerning collaborative filtering based algorithms. Then we go over one of the most popular collaborative filtering algorithms for implicit data and implement it in Python with an example dataset.

This article will explain the basic concepts of each category of the NoSQL database models and analyze the characteristics of the data that each category of the NoSQL database is suitable for processing.

In this article, we look at SQL, NoSQL and NewSQL database technologies. We go over the origin, strengths, weaknesses, application areas, and fundamental concepts for each of the database types to make it easy to select the most appropriate database type for a given application.