3 papers
cs.IR2026
Tuning-Free LLM Can Build A Strong Recommender Under Sparse Connectivity And Knowledge Gap Via Extracting Intent
Wenqing Zheng, Noah Fatsi, Daniel Barcklow +5
Recent advances in recommendation with large language models (LLMs) often rely on either commonsense augmentation at the item-category level or implicit intent modeling on existing…
cs.IR2026
Revisiting RAG Retrievers: An Information Theoretic Benchmark
Wenqing Zheng, Dmitri Kalaev, Noah Fatsi +5
Retrieval-Augmented Generation (RAG) systems rely critically on the retriever module to surface relevant context for large language models. Although numerous retrievers have recent…
cs.LG2025
Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks
Rizal Fathony, Igor Melnyk, Owen Reinert +3
User event modeling plays a central role in many machine learning applications, with use cases spanning e-commerce, social media, finance, cybersecurity, and other domains. User ev…