This article continues the discussion on the evolution of multi-task learning-based large-scale recommender systems. We take a look at strategies from Kuaishou, Tencent, YouTube, Facebook, and Amazon Prime Video to disentangle input space and address systematic biases. The article ends with sharing several tips and learnings for professionals working in this domain.

This article introduces the multi-task learning paradigm adopted by various large-scale video recommender systems. It introduces a general setup for such an MTL-based recommender. It highlights several associated challenges and describes solutions adopted by various state-of-the-art recommenders in the industry.

This article provides an introduction and literature review for multi-task learning based recommender systems. We learn how to discover task relations, design MTL architectures and overcome some of the associated challenges.

Modeling users’ past historical interactions or behavior sequences is an essential task for domains like recommender systems, click-through rate prediction, targeted advertisement, and more. This article provides a comprehensive introduction to the user behavior modeling paradigm along with highlighting several relevant and recent research works.

The previous article did a deep dive into the prompting-based pointwise, pairwise, and listwise techniques that directly use LLMs to perform reranking. In this article, we will take a closer look at some of the shortcomings of the prompting methods and explore the latest efforts to train ranking-aware LLMs. The article also describes several strategies to build effective and efficient LLM-based rerankers.

Large Language Models (LLMs) have demonstrated impressive zero-shot performance on a wide variety of NLP tasks. Recently, there has been a growing interest in applying LLMs to zero-shot text ranking. This article describes a recent paradigm that uses prompting-based approaches to directly utilize LLMs as rerankers in a multi-stage ranking pipeline.