Two-tower model is widely adopted in industrial-scale retrieval and ranking workflows across a broad range of application domains, such as content recommendations, advertisement systems, and search engines. It is also the current go-to state-of-the-art solution for pre-ranking tasks. This article explores the history and current state of the Two Tower models and also highlights potential improvements proposed in some of the recently published literature. The goal here is to help understand what makes the Two Tower model an appropriate choice for a bunch of applications, and how it can be potentially extended from its current state.

Modern time series forecasting requires a model to learn from multiple related time series. These time series often number in thousands or millions. Traditional statistical models do not scale well to these settings because they learn individual series in isolation and do not share parameters across series. Various deep learning models have been proposed recently with different inductive biases to work effectively under these settings. This article explores some of the most popular advances in deep learning architectures for modern time series forecasting.

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.

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.