Introduction

Large-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.

Recognizing feelings in the conversation partner and replying empathetically is a trivial skill for humans. But how can we infuse empathy into responses generated by a conversational dialogue agent or any of the text generation algorithm in Natural Language Processing? In this article, I will describe what empathy means through the lens of various academic disciplines and then do an in-depth review of the prior and current state-of-the-art NLU systems that can simulate empathy.

Identifying patterns among questions is quite an effective strategy when you are grinding LeetCode in preparation for your upcoming software engineering interviews. In this article, you will develop intuitions about Sliding Window pattern. You will also get a template approach to write code to solve these problems. I will also walk you through some LeetCode questions to show how to apply the template and at the end, there will be some LeetCode exercises for you to practice what you learn.