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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 curated collection of COVID-19 online datasets
Isa Inuwa-Dutse, Ioannis Korkontzelos · 2020-07-20 · via cs.IR updates on arXiv.org

One of the defining moments of the year 2020 is the outbreak of Coronavirus Disease (Covid-19), a deadly virus affecting the body's respiratory system to the point of needing a breathing aid via ventilators. As of June 21, 2020 there are 12,929,306 confirmed cases and 569,738 confirmed deaths across 216 countries, areas or territories. The scale of spread and impact of the pandemic left many nations grappling with preventive and curative approaches. The infamous lockdown measure introduced to mitigate the virus spread has altered many aspects of our social routines in which demand for online-based services skyrocketed. As the virus propagate, so does misinformation and fake news around it via online social media, which seems to favour virality over veracity. With a majority of the populace confined to their homes for a long period, vulnerability to the toxic impact of online misinformation is high. A case in point is the various myths and disinformation associated with the Covid-19, which, if left unchecked, could lead to a catastrophic outcome and hamper the fight against the virus. While the scientific community is actively engaged in identifying the virus treatment, there is a growing interest in combating the associated harmful infodemic. To this end, researchers have been curating and documenting various datasets about Covid-19. In line with existing studies, we provide an expansive collection of curated datasets to support the fight against the pandemic, especially concerning misinformation. The collection consists of 3 categories of Twitter data, information about standard practices from credible sources and a chronicle of global situation reports. We describe how to retrieve the hydrated version of the data and proffer some research problems that could be addressed using the data.