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.

Advances in the contrastive learning domain have made it possible to reduce the performance gap between self-supervised and supervised learning methods. Consequently, it has enabled the potential of utilizing the vast amount of unlabeled big data. This article goes over state-of-the-art contrastive learning methods for effective video representation learning.

Defining positive and negative labels for a retrieval task in a search ranking system is a non-trivial problem. This article goes over various sampling strategies for creating negative and positive pairs for effective representation learning. It introduces the concept of mining hard examples, followed by various strategies to sample hard positive and hard negative pairs. The article includes a lot of tips and learnings based on heuristics and empirical results from a comprehensive set of research papers published across the industry and academia.

Large Language Models (LLMs), like GPT-x, PaLM, BLOOM, have shaken up the NLP domain and completely redefined the state-of-the-art for a variety of tasks. One reason for the popularity of these LLMs has been their out-of-the-box capability to produce excellent performance with none to little domain-specific labeled data. The information retrieval community is also witnessing a revolution due to LLMs. These large pre-trained models can understand task instructions specified in natural language and then perform well on tasks in a zero-shot or few-shot manner. In this article, I review this theme and some of the most prominent ideas proposed by researchers in the last few months to enable zero/few-shot learning in text retrieval and ranking applications like search ranking, question answering, fact verification, etc.