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.

Diffusion Models have exhibited state-of-the-art results in image and audio synthesis domains. A recent line of research has started to adopt Diffusion for recommender systems. This article introduces Diffusion and its relevance to the recommendations domain and also highlights some of the most recent proposals on this novel theme.

Recent developments in Large Language Models (LLMs) have brought a significant paradigm shift in Natural Language Processing (NLP) domain. These pretrained language models encode an extensive amount of world knowledge, and they can be applied to a multitude of downstream NLP applications with zero or just a handful of demonstrations.

While existing recommender systems mainly focus on behavior data, large language models encode extensive world knowledge mined from large-scale web corpora. Hence these LLMs store knowledge that can complement the behavior data. For example, an LLM-based system, like ChatGPT, can easily recommend buying turkey on Thanksgiving day, in a zero-shot manner, even without having click behavior data related to turkeys or Thanksgiving.

Many researchers have recently proposed different approaches to building recommender systems using LLMs. These methods convert different recommendation tasks into either language understanding or language generation templates. This article highlights the prominent work done on this theme.

Twitter has open-sourced a majority of its recommendation algorithm. It offers an exciting opportunity for researchers, industry practitioners, and RecSys enthusiasts to take a close look at how Twitter computes the recommended feed for the For You page. This article described Twitter’s end-to-end recommender system along with relevant literature and code references.