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 2
Optimal Re-Ranking Depth
Second-stage neural rankers are commonly applied with a fixed re-ranking depth, assuming that retrieval effectiveness saturates as depth increases.One Basket, Many Experts: Decorrelated Re-ranking for Financial Asset Recommendation
Financial asset recommendation for retail investors is typically optimized for relevance, that is, how well a recommendation matches what each user will buy.From Waste to Discovery: Recycling High-Scored Unexposed Candidates in Taobao Feed Recommendation
Helping users discover new interests is vital to the long-term health of industrial recommender systems, yet the usual remedy, boosting exposure of under-explored (discovery) content, often…Do Search-Augmented Recommenders Use the Latest Search Episode?
Search-augmented recommenders can use the latest search episode and earlier searches.Generative Retrieval under Real-Time Supply Constraints in Local Commerce
Generative retrieval predicts semantic identifiers for candidate entities.PMRecBench: A Benchmark for Personalization in Recommendation Agents under Multimodal Queries
On Web platforms for shopping and entertainment, recommendation agents under multimodal queries aim to deliver personalized recommendations that satisfy users' visually specified requirements and…ArcRAG: Agentic Compositional Retrieval over Knowledge Graphs
Graph-based retrieval-augmented generation (GraphRAG) answers questions by retrieving over a knowledge graph built from a corpus.Signed but Steerable: Measuring and Closing the Text-Vector Integrity Gap in Offline Retrieval-Augmented Generation
Offline retrieval-augmented generation (RAG) on edge devices is updated through signed knowledge packs that carry text and precomputed embedding vectors.Deliberate Latent Reasoning for Sequential Recommendation
Sequential recommenders are widely applied in real-world scenarios to model user preferences and behavior patterns based on user interactions.Alternatives to Shallow Autoencoders for Collaborative Filtering
Collaborative filtering (CF) is a cornerstone of recommender systems and plays a relevant role in many modern applications.WISE: An Agentic Framework for Whole-page Multimodal Search Experience Attribution
Algorithm-driven services, including search, recommendation, and advertising, rely on multi-stage model pipelines.Balancing hierarchical representation and quantization: A performance trade-off in text embeddings
Hierarchical text embedding models offer variable-length representations, while quantization reduces numerical precision; both aim to improve computational efficiency.MapHub: Daily Relevance Heatmaps of New arXiv Preprints for a Research Community
MapHub, the MIDAS arXiv Preprint Hub, assesses each day's new arXiv submissions for every participant in a research community, with a large language model reading each paper against portfolios built…IDs That Mean Something: A Hands-On Tutorial on Semantic IDs for Recommender Systems
Traditional recommender systems rely on arbitrary integer IDs to identify items, which carry no semantic information and force models to learn representations from interaction data.IntentGEO: Intent-aware Generative Engine Optimization for Recommendation
Large language models (LLMs) increasingly shape recommendations across chatbots, e-commerce, and user-generated content (UGC) platforms, creating demand for Generative Engine Optimization (GEO) to…ReuseGR: Offline--Online Decoupled Generative Retrieval via Adaptive Prefix Reuse
Larger generative retrieval models can improve retrieval quality, but their decoding cost makes low-latency serving difficult.OSGR: Observation-Support Graph Reasoning for Earth Observation Question Answering
Answering geographic queries over Earth observation (EO) archives requires solving a joint retrieval-and-derivation problem under strict spatiotemporal constraints.Do Retrieval Ranking Models Capture Event Order? A Study of Timeline-Constrained Retrieval
Semantic retrieval models aim to match documents to a query's "meaning", yet it remains unclear whether they successfully encode temporal relations between events in their ranking mechanisms.DualEvolve: Dual-Level Self-Evolution of Mechanism and Orchestration for Recommendation
Recommender systems are widely used in e-commerce and content platforms, where improvement requires repeated model design, training optimization, and experimental validation.SourceFlow: Collaborative Source Coupling for Flow-Based Recommendation
Diffusion-based recommenders learn user preferences by denoising interaction histories, but the added noise can distort the original preference signals.