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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
Los perfiles de investigación y su implantación en la Uni...
Manuel Ruiz de Luzuriaga Peña, Isabel Muñoz Mouriño, Mercedes Bo · 2020-05-29 · via cs.IR updates on arXiv.org

This work aims to monitor and control the presence of UPNA research staff in the main research profiles platforms, not only in the most obvious ones such as Google Scholar Citation, Researcher ID, Scopus ID and ORCID, but also in other services that, in practice, they function as research profiles, such as Mendeley, Linkedin, ResearchGate, Academia.edu and Academica-e. We also find it interesting to analyze that presence and see how it responds to a variables, such as the department, gender, job category, research group. In this study we have excluded some platforms for different reasons. Dialnet profiles are entered from the UPNA library (BUPNA), which means that all those who meet the requirements for inclusion would be there, so their analysis does not make much sense, since it depends on factors outside the will of the researcher himself. The same is the case with the UPNA Scientific Production Portal (PPC): the data is entered from the Vicerrectorado de Investigación and should include all members of the UPNA PDI. Using as a base the census of university research staff provided by the Vicerrectorado de Investigación, it has been verified, for each author, the existence or not of a profile in the different services studied. The results have been tabulated in an Excel file to be able to analyze them later. The data has been collected in March 2018 for Orcid, ResearcherID, ScopusID, Google Scholar Citations and Mendeley. In November 2018, data from Academica-e, Academia.edu, ResearchGate and Linkedin were taken. For each of the profiles, a search by institutional affiliation was used, when possible, to obtain a first list of UPNA research personnel with that profile. Subsequently, a search was carried out, person by person, of the rest of the research staff that did not appear in that first list.