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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 Recommendations JFinTEB: Japanese Financial Text Embedding Benchmark UsefulBench: Towards Decision-Useful Information as a Target for Information Retrieval SIMMER: Cross-Modal Food Image--Recipe Retrieval via MLLM-Based Embedding Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts Collaborative Filtering Through Weighted Similarities of User and Item Embeddings IG-Search: Step-Level Information Gain Rewards for Search-Augmented Reasoning Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model Evaluation of Agents under Simulated AI Marketplace Dynamics
The Landscape of Data Reuse in Interactive Information Re...
Tianji Jiang, Wenqi Li, Jiqun Liu · 2024-11-23 · via cs.IR updates on arXiv.org

Sharing and reusing research data can effectively reduce redundant efforts in data collection and curation, especially for small labs and research teams conducting human-centered system research, and enhance the replicability of evaluation experiments. Building a sustainable data reuse process and culture relies on frameworks that encompass policies, standards, roles, and responsibilities, all of which must address the diverse needs of data providers, curators, and reusers. To advance the knowledge and accumulate empirical understandings on data reuse, this study investigated the data reuse practices of experienced researchers from the area of Interactive Information Retrieval (IIR) studies, where data reuse has been strongly advocated but still remains a challenge. To enhance the knowledge on data reuse behavior and reusability assessment strategies within IIR community, we conducted 21 semi-structured in-depth interviews with IIR researchers from varying demographic backgrounds, institutions, and stages of careers on their motivations, experiences, and concerns over data reuse. We uncovered the reasons, strategies of reusability assessments, and challenges faced by data reusers within the field of IIR as they attempt to reuse researcher data in their studies. The empirical finding improves our understanding of researchers' motivations for reusing data, their approaches to discovering reusable research data, as well as their concerns and criteria for assessing data reusability, and also enriches the on-going discussions on evaluating user-generated data and research resources and promoting community-level data reuse culture and standards.