STARS is a Transformer-based sequential recommendation framework designed for large-scale e-commerce. It addresses cold-start items, diverse user intent, and latency constraints by combining LLM-augmented item semantics, dual-memory user embeddings, context-aware scoring, and an efficient two-stage retrieval pipeline.

This paper introduces WalkRAG, a spatial Retrieval-Augmented Generation (RAG) framework that leverages Large Language Models (LLMs) to recommend personalized and walkable urban itineraries. It addresses known LLM limitations in spatial reasoning and factual accuracy by integrating spatial and contextual urban knowledge for enhanced route generation and point-of-interest information retrieval.

This survey provides a comprehensive analysis of recent advancements in embedding techniques for recommender systems, covering centralized approaches like matrix, sequential, and graph-based models, along with solutions for scalability and emerging LLM-enhanced methods. It highlights their role in capturing complex relationships and improving recommendation performance.