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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
A Digital Ecosystem for Animal Movement Science: Making a...
Brendan Hoover, Gil Bohrer, Jerod Merkle, Jennifer A. Miller · 2020-04-13 · via cs.IR updates on arXiv.org

Movement is a fundamental aspect of animal life and plays a crucial role in determining the structure of population dynamics, communities, ecosystems, and diversity. In recent years, the recording of animal movements via GPS collars, camera traps, acoustic sensors, and citizen science, along with the abundance of environmental and other ancillary data used by researchers to contextualize those movements, has reached a level of volume, velocity, and variety that puts movement ecology research in the realm of big data science. That data growth has spawned increasingly complex methods for movement analysis. Consequently, animal ecologists need a greater understanding of technical skills such as statistics, geographic information systems (GIS), remote sensing, and coding. Therefore, collaboration has become increasingly crucial, as research requires both domain knowledge and technical expertise. Datasets of animal movement and environmental data are typically available in repositories run by government agencies, universities, and non-governmental organizations (NGOs) with methods described in scientific journals. However, there is little connectivity between these entities. The construction of a digital ecosystem for animal movement science is critically important right now. The digital ecosystem represents a setting where movement data, environmental layers, and analysis methods are discoverable and available for efficient storage, manipulation, and analysis. We argue that such a system which will help mature the field of movement ecology by engendering collaboration, facilitating replication, expanding the spatiotemporal range of potential analyses, and limiting redundancy in method development. We describe the key components of the digital ecosystem, the critical challenges that would need addressing, as well as potential solutions to those challenges.