In previous articles, I gave an introduction to graph representation learning and highlighted several shallow methods for learning homogeneous graph embeddings. This article focuses on shallow representation learning methods for heterogeneous graphs.
While homogeneous networks have only one type of nodes and edges, heterogeneous networks contain different types of nodes or edges. So, a homogeneous network can also be considered as a special case of a heterogeneous network. Heterogeneous networks, also called heterogeneous information networks (HIN), are ubiquitous in real-world scenarios.
Instruction-tuning methods enable open-source Large Language Models (LLMs) usage for building highly effective recommender systems on private data. This article highlights the latest research work on this paradigm of using LLMs for recommendation tasks.
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.