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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
BaBar - A Community Web Site in an Organizational Setting
Ray Cowan, Yogesh Deshpande, Bebo White · 2003-06-16 · via cs.IR updates on arXiv.org

The BABAR Web site was established in 1993 at the Stanford Linear Accelerator Center (SLAC) to support the BABAR experiment, to report its results, and to facilitate communication among its scientific and engineering collaborators, currently numbering about 600 individuals from 75 collaborating institutions in 10 countries. The BABAR Web site is, therefore, a community Web site. At the same time it is hosted at SLAC and funded by agencies that demand adherence to policies decided under different priorities. Additionally, the BABAR Web administrators deal with the problems that arise during the course of managing users, content, policies, standards, and changing technologies. Desired solutions to some of these problems may be incompatible with the overall administration of the SLAC Web sites and/or the SLAC policies and concerns. There are thus different perspectives of the same Web site and differing expectations in segments of the SLAC population which act as constraints and challenges in any review or re-engineering activities. Web Engineering, which post-dates the BABAR Web, has aimed to provide a comprehensive understanding of all aspects of Web development. This paper reports on the first part of a recent review of application of Web Engineering methods to the BABAR Web site, which has led to explicit user and information models of the BABAR community and how SLAC and the BABAR community relate and react to each other. The paper identifies the issues of a community Web site in a hierarchical, semi-governmental sector and formulates a strategy for periodic reviews of BABAR and similar sites.