Applied & Human-Centered · Computer Science & AI
Information Retrieval
The newest papers in Information Retrieval that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
979 papers, from 4 September 2026 to 5 November 2026: 979 arXiv preprints.
Get the few papers worth your time in Information Retrieval every morning.
Follow Information Retrieval and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 12
Do Similarity Measures Measure Similarity? How User Activity Creates Phantom Correlation in Recommender Systems
Item-item similarity is one of the oldest and most foundational signals in recommender systems: two items are assumed to be similar when the same users consume them.ACARE: Agent-Context-Aware Reranker for Iterative Search
In agentic search, a document can match an agent-generated sub-query without helping answer the user's original question.RELAY: One Query, Two Roles for Sequential Recommendation
Sequential recommendation must combine semantic preferences with behavioral associations to deliver accurate rankings at low inference cost.When Connectivity Misleads: Guiding Agentic Search with Contrastive Evidence Paths
Search agents can use cross-document graphs to discover sources through multi-hop connections and choose subsequent actions based on candidate rankings and relationships within a limited observation…Is the education gap in generative AI use smaller than the internet's was at a similar level of use? Evidence from 16 European countries
New technologies are usually adopted first by people with more formal education.Counterfactual Profile Perturbations for Recommendation Explanations: A Reproducibility Study
This reproducibility paper explores the counterfactual explanation technique known as Learning to eXplain Recommendations (LXR).BehaveRec: When Does Long-Term Behavioral History Help Sequential Recommendation? A Study with Session, History, and Community Signals
Sequential recommendation models typically predict a user's next action from recent session interactions, which limits personalization when short-term signals are weak or ambiguous.Preserving rare-item recall under a storage budget: quantization, distillation, and the limits of teacher fidelity
Under a fixed embedding-storage budget, recommenders can use a small full-precision model, a distilled student, or a higher-rank low-precision model.From Generation to Scoring: Scaling LLM-Based Semantic Labeling at LinkedIn
For efficiency, industrial large language model (LLM) labelers often pack many candidate labels into one prompt and generate a YES/NO answer for each, even when each decision depends only on the…MAREval: A Multi-Agent Framework for Evaluating Natural Language Recommendation Explanations
Evaluating natural language explanations in recommender systems is essential for fostering user trust, transparency, and engagement.Cross-Day Compatibility of Daily Item Embeddings: An Empirical Study of Alignment, Scale Management, and CTR Adaptation
Daily item co-occurrence embeddings are paired with next-day behavior without future-behavior leakage.Relevance-Guided Residual Calibration for Spectral Compression of Dense Retrieval Embeddings
Spectral compression reduces the storage and scoring costs of dense retrieval by projecting embeddings onto a smaller principal subspace.Decision-Native Recommendation with Large Language Models
Large language models (LLMs) bring world knowledge to user preference modeling in sequential recommendation, but generation-based recommenders express next-item predictions indirectly as text.LoopGR: Parameter-Efficient Scaling for Generative Recommendation via Recurrent Depth
Generative recommendation formulates item recommendation as autoregressive generation rather than a discriminative scoring problem.MIDA: Multi-Interest Decoding with Adaptive Aggregation for Session-based Recommendation
Session-based recommendation (SBR) infers user interest from limited interaction sequences, yet current models show a consistent performance drop when longer histories are available.$Δτ$-Bench: Pushing Agents Toward a Moving Capability
In this paper, we introduce $Δτ$-Bench, an executable agent benchmark that models the structural evolution of large tool ecosystems.Recovering Business Rules as Cascade Lookups for Enterprise Field Prediction
Enterprise sales-order systems rely on manually maintained configuration rules to determine fields such as payment terms, shipping points, and sales ownership.Factual Yet One-Sided: Stance Poisoning and Evidence Balancing in Retrieval-Augmented Generation
The stance expressed in responses from retrieval-augmented generation systems is significantly influenced by the stance distribution of the retrieved materials.Cross-Lingual Information Retrieval in Tetun for Ad-Hoc Search
Developing information retrieval (IR) systems that enable access across multiple languages is crucial in multilingual contexts.A Domain-Aware Pipeline for Structured Extraction and Retrieval-Augmented Question Answering over arXiv Superconductivity Literature
Scientific literature in materials physics is growing faster than researchers can manually inspect it, yet most retrieval tools rely on keyword matching or generic question answering that ignores…