The field of information retrieval and recommendation is witnessing a significant shift towards leveraging Large Language Models (LLMs) to improve the accuracy and efficiency of various tasks. Researchers are exploring innovative ways to integrate LLMs into existing models, such as latent topic modeling and recommendation explanation generation, to enhance their performance. The use of LLMs is also being investigated for automating relevance judgments and query expansion, which are crucial components of information retrieval systems. Notably, the alignment of LLMs with specific tasks is emerging as a key factor in achieving optimal results. Overall, the field is moving towards developing more sophisticated and efficient LLM-based approaches that can effectively address the challenges of information retrieval and recommendation. Noteworthy papers include: Retrieval-Augmented Recommendation Explanation Generation with Hierarchical Aggregation, which proposes a novel method for generating recommendation explanations using LLMs. Aligned Query Expansion, which introduces a new approach to query expansion that leverages LLM alignment to improve retrieval effectiveness.