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.
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Rethinking Positional Encoding and Interaction Granularity for CTR Prediction
Click-through rate (CTR) prediction relies on historical behaviors and contextual features to estimate clicks on a target item.Coverage Under Contradiction: A Benchmark for Negation-Aware Retrieval
Coverage-evaluation benchmarks quantify whether retrieved evidence covers the different pieces of information needed to satisfy an information need.Lossless Compression of Proximity Graph Indexes for Efficient Approximate Nearest Neighbors Search
Graph-based Approximate Nearest Neighbor (ANN) indexes, such as HNSW, are the backbone of modern vector search.AutoRSE: Evaluating LLM Agents as Recommendation Engineers for Self-Evolving Recommender Systems
Recommender Systems Engineer Agents (RSE-Agents), which use LLM-based agents to autonomously improve recommendation algorithms, are emerging as a promising paradigm.TaskBridge: Technical Report for SIGIR 2026 eCommerce Challenge
The SIGIR 2026 eCommerce Challenge Query Reformulation Task evaluates the reformulation of task-oriented queries into expressions suitable for product retrieval, followed by BM25-based retrieval.GradCO: Behavior-Aware Gradient-Conflict Offloading for Text-Based Person Anomaly Search
Text-based Person Anomaly Search (TPAS) retrieves a target pedestrian using natural-language descriptions of appearance and normal or anomalous behavior.Modeling Preference Dependencies with Directed Acyclic Graphs for Narrative-Driven Recommendation
Narrative-driven recommendation allows users to express multifaceted needs through free-form text where stated preferences naturally qualify and condition one another.LESSRec: Level-Specific History Modeling for Semantic ID-Based Recommendation
Semantic ID-based recommendation represents each item as a sequence of semantic tokens obtained through residual quantization.When Is Additional Retrieval Worthwhile? A Cost-Aware Study of Evidence Gain and Answer Reliability in RAG
Retrieval-augmented generation (RAG) can benefit from more sophisticated retrieval, but extra retrieval may be unnecessary for many questions and gains in evidence retrieval may not translate into…Can Complementary Signals Bridge Similarity Islands? Manifold-Augmented Graph Embedding for Multimodal Recommendation
Multimodal Recommender Systems (MMRS) leverage rich item features to mitigate data sparsity, yet similarity-driven graph construction and propagation can favor semantically similar substitutes over…LCM-Promo: An LLM-Based Uplift Model for Promotion Allocation
We present LCM-Promo, an LLM-based uplift model for promotion allocation.REGER: Modeling inter-item Relations with Semantic IDs for Generative Recommendation
Personalized recommendation depends on learning semantic and collaborative relations among items from online content and user interactions.Beyond Similarity Search: Knowledge Graph Metadata Enrichment for Enterprise Retrieval-Augmented Generation
A key challenge of RAG systems is retrieving information from enterprise documents created by multiple teams over time, where it is not possible to enrich the information using the latest…DeFT-RAG: Decomposing Multi-Hop Questions into Self-Contained Sub-Questions with Reusable Answers
Retrieval-augmented generation (RAG) lets a large language model (LLM) answer questions by retrieving relevant passages from a large corpus.Measuring Where Intent Gets Lost in Agentic Smart Contract Synthesis
AI agents can now write software from a plain-English description, but code that runs is not the same as code that does what was asked.What Constitutes Food Similarity? Visual Representations and Retrieval in Indian Cuisine
Food similarity underlies retrieval, recommendation, substitution, and other food computing tasks, yet food representations are typically evaluated by identity preservation.Statistical Detection of Positional Bias in LLM-Based Recommender Systems: Methodologies and Empirical Evidence
Large Language Models (LLMs) are increasingly used as recommendation generators in conversational recommender systems (RS) via retrieval-augmented generation (RAG).Merge and Branch: Efficient Tree-Structured Supervised Fine-Tuning of LLMs for Recommendation
LLM-based generative recommendation reads long, text-rich interaction histories but generates only short semantic identifiers (SIDs).Agent Memory as Recommendation: Recommender-System Design for Long-Term Memory of LLM Agents
Large language model (LLM) agents that serve users across many sessions must choose, for every request, the few stored memories that enter a limited context window.Knowing When the Evidence Is Enough: Requirement-Directed Search with Horn-Closure Certificates
After each Web search, a search-augmented model must decide whether its evidence suffices: answer, search again, or abstain.