Applied & Human-Centered · Computer Science & AI
Information Retrieval
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979 papers, from 4 September 2026 to 5 November 2026: 979 arXiv preprints.
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Newest papers, page 7
Enhancing Knowledge Entity Relation Extraction in Abstracts and Full-text Content of Academic Articles Using Large Language Models
This study investigates the effectiveness of large pre-trained language models (LLMs) for knowledge entity relation extraction (KERE) from academic papers.Towards Trustworthy Multi-stakeholder Recommender Systems
Recommender system (RS) nowadays has to reflect, combine, and solve often contrasting requirements and expectations from dozens of stakeholders.Search When Needed, Proceed When Reliable: Metacognitive Reliability Supervision for Efficient Agentic Search
Efficient agentic search requires large language models (LLMs) to determine whether they can reliably proceed with their current knowledge or need additional external evidence.Peeling the Matryoshka: Extracting Ontological Structure from Retrieval Embeddings
Taxonomy construction is essential for knowledge-intensive NLP yet remains costly: clustering-based methods (i) need careful per-corpus tuning and yield unstable hierarchies, while LLM-based…Completeness and Cost of Retrieval Structures for Multi-Tool Requests in LLM Agents
An agent that must call several tools to fulfill a request fails when the tool retriever omits any one of them, yet tool retrievers are mostly built and evaluated on queries requiring a single tool.Recommendation Feedback Loops Shape Urban Income Segregation and Venue Visitation
Location-based recommender systems shape where people go in cities and learn from the visits they steer, forming a human-AI feedback loop.PatchScout: Thematic Web Data Collection via Information Foraging
Researchers often need web corpora that are not answers to a single query, but reusable collections of documents spanning many decentralized sources.MEMENTO: Learning Context Management for Long Context Processing Agents
LLM agents that operate over long inputs must perform context management: deciding what to retain in a fixed-size context window.PALATE: Pretrained Autoregressive LATent Embeddings
Local-commerce and food-delivery marketplaces run large ads businesses: across hundreds of thousands of stores, merchants pay to be surfaced on browse and search surfaces, and a ranker decides which…PRISM: Personalized Item Representations in User History
Sequential recommenders typically construct history inputs from item identifiers or content features using an embedding map shared across users.Read More, Reason Deeper: Latent Workspaces for Long-Sequence Recommendation
Long-sequence recommendation can improve along two axes: \emph{evidence scaling} exposes longer histories, while \emph{reasoning scaling} adds layers that interpret that evidence for a candidate.A Sequential Decision Framework for Evaluating LLM-Guided Search
Recent systems applying retrieval-augmented generation (RAG) and "deep research" iteratively generate queries, retrieve documents, and examine their contents, using the resulting information to…CASH: A Capability-Aware Subsidy Harness for Advertiser Subsidy Allocation
Advertising platforms subsidize advertisers through billing discounts, and Advertiser Subsidy Allocation (ASA) chooses a subsidy level for every account to maximize total discounted revenue under a…PFA: Attention Can Be Progressively Focused in Long-Sequence Generative Recommendation
Long user behavioral sequences improve the performance of generative recommendation (GR), but computing full attention over long histories during autoregressive beam search decoding incurs…Federated Recommender Agents
LLM-powered recommender agents use reasoning and memory for personalization, but centralized access to user information raises privacy concerns, while isolated agents cannot learn from others'…Does the Citation Matter? Quality, Grounding, and Physician-Rubric Accuracy of Google's AI Overviews for Medical Information
Google's AI Overviews (AIOs) are the first result users encounter when searching the web, shifting the work of selecting and evaluating sources from the user to Gemini.Try Before You Evolve: Ranking-Scored Trials for Recursive Self-Improvement of Recommender Systems
Recommenders on Web platforms such as e-commerce and music streaming need their training recipes re-tuned for each dataset.UGR: Unified Generative Ranking for Heterogeneous E-commerce Recommendation
List-wise ranking in e-commerce recommendation must balance user utility and commercial value across organic content, ads, and candidates from other businesses, while modeling candidate interactions…RoFiGR: Coarse Routing, Fine Refinement via Level-Adaptive Distillation in Multimodal Generative Recommendation
Generative recommenders predict an item's semantic identifier (SID) one code at a time.Lost in the Latent Space: How Code Embeddings Represent Functional Intent and Implementation Fidelity
Code embedding models have become a key building block for many software engineering applications, yet their internal representations remain poorly understood.