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.
Auto-regressive search engines emerge as a promising paradigm for next-gen information retrieval systems. This article introduces this generative retrieval and the various latest techniques that have been proposed to improve its effectiveness.
Large Language Models (LLMs) have emerged as viable tools for various recommendation tasks. This article highlights various methods for incorporating users, items, and behavior data into the instructions for LLMs.
