23.6k citations
- Carnegie Mellon UniversityUS49 papers
- Google (United States)US37 papers
- California Institute of TechnologyUS33 papers
- Amazon (Germany)DE32 papers
- University of Illinois Urbana-ChampaignUS22 papers
- Microsoft (United States)US20 papers
- Stanford UniversityUS20 papers
- The University of Texas at AustinUS20 papers
- University of California, Los AngelesUS20 papers
- University of California, Santa BarbaraUS19 papers
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- Georgia Institute of TechnologyUS17 papers
6 papers · 2 filters
AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents
Mingdai Yang, Nurendra Choudhary, Jiangshu Du +4
Recent agent-based recommendation frameworks aim to simulate user behaviors by incorporating memory mechanisms and prompting strategies, but they struggle with hallucinating non-ex…
Multi-modal Relational Item Representation Learning for Inferring Substitutable and Complementary Items
Junting Wang, Chenghuan Guo, Jiao Yang +3
We study the problem of inferring substitutable and complementary items, which underpins applications such as alternative and follow-up purchase suggestions. Existing approaches ty…
Hierarchical Lexical Graph for Enhanced Multi-Hop Retrieval
Abdellah Ghassel, Ian Robinson, Gabriel Tanase +6
Retrieval-Augmented Generation (RAG) grounds large language models in external evidence, yet it still falters when answers must be pieced together across semantically distant docum…
Knowledge Distillation for Enhancing Walmart E-commerce Search Relevance Using Large Language Models
Hongwei Shang, Nguyen Vo, Nitin Yadav +6
Ensuring the products displayed in e-commerce search results are relevant to users queries is crucial for improving the user experience. With their advanced semantic understanding,…
Exploring Sparsification for Inference-free Sparse Retrievers
Xinjie Shen, Zhichao Geng, Yang Yang
With increasing demands for efficiency, information retrieval has developed a branch of sparse retrieval, further advancing towards inference-free retrieval where the documents are…
PCL: Prompt-based Continual Learning for User Modeling in Recommender Systems
Mingdai Yang, Fan Yang, Yanhui Guo +6
User modeling in large e-commerce platforms aims to optimize user experiences by incorporating various customer activities. Traditional models targeting a single task often focus o…