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cs.IR updates on arXiv.org

From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora Personalized Benchmarking: Evaluating LLMs by Individual Preferences Modular Representation Compression: Adapting LLMs for Efficient and Effective 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Benchmarking RAG and GraphRAG for Agentic Search Systems Hydra: Unifying Document Retrieval and Generation in a Single Vision-Language Model SocialWise: LLM-Agentic Conversation Therapy for Individuals with Autism Spectrum Disorder to Enhance Communication Skills Working Notes on Late Interaction Dynamics: Analyzing Targeted Behaviors of Late Interaction Models Resolving the Robustness-Precision Trade-off in Financial RAG through Hybrid Document-Routed Retrieval Spectral Tempering for Embedding Compression in Dense Passage Retrieval AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation To LLM, or Not to LLM: How Designers and Developers Navigate LLMs as Tools or Teammates A Domain-Specific Language for LLM-Driven Trigger Generation in Multimodal Data Collection MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search Hunt Globally: Wide Search AI Agents for Drug Asset Scouting in Investing, Business Development, and Competitive Intelligence From Speech-to-Spatial: Grounding Utterances on A Live Shared View with Augmented Reality Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics Exploring Structural Complexity in Normative RAG with Graph-based approaches: A case study on the ETSI Standards SRBench: A Comprehensive Benchmark for Sequential Recommendation with Large Language Models MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning Retrieval-Augmented Large Language Models for Evidence-Informed Guidance on Cannabidiol Use in Older Adults RLPO: Residual Listwise Preference Optimization for Long-Context Review Ranking When & How to Write for Personalized Demand-aware Query Rewriting in Video Search Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows WisPaper: Your AI Scholar Search Engine GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs Hierarchical Semantic Retrieval with Cobweb WARBERT: A Hierarchical BERT-based Model for Web API Recommendation Reliable Evaluation Protocol for Low-Precision Retrieval VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context From Limited Labels to Open Domains:An Efficient Learning Method for Drone-view Geo-Localization User Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data
Greedy Optimized Multileaving for Personalization
Kojiro Iizuka, Takeshi Yoneda, Yoshifumi Seki · 2019-07-19 · via cs.IR updates on arXiv.org

Personalization plays an important role in many services. To evaluate personalized rankings, online evaluation, such as A/B testing, is widely used today. Recently, multileaving has been found to be an efficient method for evaluating rankings in information retrieval fields. This paper describes the first attempt to optimize the multileaving method for personalization settings. We clarify the challenges of applying this method to personalized rankings. Then, to solve these challenges, we propose greedy optimized multileaving (GOM) with a new credit feedback function. The empirical results showed that GOM was stable for increasing ranking lengths and the number of rankers. We implemented GOM on our actual news recommender systems, and compared its online performance. The results showed that GOM evaluated the personalized rankings precisely, with significantly smaller sample sizes (< 1/10) than A/B testing.

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