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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
Challenges in Docker Development: A Large-scale Study Usi...
Mubin Ul Haque, Leonardo Horn Iwaya, M. Ali Babar · 2020-08-11 · via cs.IR updates on arXiv.org

Docker technology has been increasingly used among software developers in a multitude of projects. This growing interest is due to the fact that Docker technology supports a convenient process for creating and building containers, promoting close cooperation between developer and operations teams, and enabling continuous software delivery. As a fast-growing technology, it is important to identify the Docker-related topics that are most popular as well as existing challenges and difficulties that developers face. This paper presents a large-scale empirical study identifying practitioners' perspectives on Docker technology by mining posts from the Stack Overflow (SoF) community. Method: A dataset of 113,922 Docker-related posts was created based on a set of relevant tags and contents. The dataset was cleaned and prepared. Topic modelling was conducted using Latent Dirichlet Allocation (LDA), allowing the identification of dominant topics in the domain. Our results show that most developers use SoF to ask about a broad spectrum of Docker topics including framework development, application deployment, continuous integration, web-server configuration and many more. We determined that 30 topics that developers discuss can be grouped into 13 main categories. Most of the posts belong to categories of application development, configuration, and networking. On the other hand, we find that the posts on monitoring status, transferring data, and authenticating users are more popular among developers compared to the other topics. Specifically, developers face challenges in web browser issues, networking error and memory management. Besides, there is a lack of experts in this domain. Our research findings will guide future work on the development of new tools and techniques, helping the community to focus efforts and understand existing trade-offs on Docker topics.