The field of recommendation systems is witnessing a significant shift towards the integration of large language models (LLMs) to improve the accuracy and effectiveness of recommendations. Researchers are exploring the potential of LLMs to enhance user experience by providing more personalized and contextually relevant recommendations. One of the key areas of focus is the development of frameworks that can effectively incorporate user reviews and preferences into the recommendation process. Additionally, there is a growing interest in using LLMs to estimate positional bias in logged interaction data, which can help mitigate the biases of prior ranking models. The use of LLMs is also being explored for complementary recommendation, where the goal is to suggest compatible items to users. Overall, the integration of LLMs in recommendation systems is opening up new avenues for innovation and improvement in the field. Noteworthy papers in this area include: Learning to Shop Like Humans, which proposes a review-driven recommendation framework that integrates user reviews into the LLM-based reranking process. Grocery to General Merchandise, which introduces a cross-pollination framework that bridges grocery and general merchandise cross-category recommendations using LLMs and real-time cart context. LLMs for estimating positional bias in logged interaction data, which proposes a novel method for estimating position bias using LLMs applied to logged user interaction data. Knowledge-Augmented Relation Learning for Complementary Recommendation with Large Language Models, which proposes a framework that strategically fuses active learning with LLMs to expand a high-quality dataset at a low cost.