With its outstanding performance, ChatGPT has become a hot topic of discussion in the NLP community and beyond. This article delves into recent efforts to harness the power of ChatGPT for recommendation tasks.

The previous article “A Guide to Graph Representation Learning” provided a comprehensive introduction to the state of graph representation learning, along with a review of the basic terminologies, techniques, and applications. If you are new to the graph learning domain, I’d highly recommend you read the previous article first. This article takes a closer look at different types of shallow graph embedding models of homogeneous graphs. It also highlights a few real-world applications that build upon some of these ideas.

In recent years, there has been a significant amount of research activity in the graph representation learning domain. These learning methods help in analyzing abstract graph structures in information networks and improve the performances of state-of-the-art machine learning solutions for real-world applications, such as social recommendations, targeted advertising, user search, etc. This article provides a comprehensive introduction to the graph representation learning domain, including common terminologies, deterministic and stochastic modeling techniques, taxonomy, evaluation methods, and applications.

The Mixture-of-Experts (MoE) is a classical ensemble learning technique originally proposed by Jacobs et. al¹ in 1991. MoEs have the capability to substantially scale up the model capacity and only introduce small computation overhead. This ability combined with recent innovations in the deep learning domain has led to the wide-scale adoption of MoEs in healthcare, finance, pattern recognition, etc. They have been successfully utilized in large-scale applications such as Large Language Modeling (LLM), Machine Translation, and Recommendations. This article gives an introduction to Mixture-of-Experts and some of the most important enhancements made to the original MoE proposal. Then we look at how MoEs have been adapted to compute recommendations by looking at examples of such systems in production.