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.

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.

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.