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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
Searching for the Internet of Things on the Web: Where It...
Ali Shemshadi, Quan Z. Sheng, Wei Emma Zhang, Aixin Sun, Yongrui · 2016-07-23 · via cs.IR updates on arXiv.org

The Internet of Things (IoT), in general, is a compelling paradigm that aims to connect everyday objects to the Internet. Nowadays, IoT is considered as one of the main technologies which contribute towards reshaping our daily lives in the next decade. IoT unlocks many exciting new opportunities in a variety of applications in research and industry domains. However, many have complained about the absence of the real-world IoT data. Unsurprisingly, a common question that arises regularly nowadays is "Does the IoT already exist?". So far, little has been known about the real-world situation on IoT, its attributes, the presentation of data and user interests. To answer this question, in this work, we conduct an in-depth analytical investigation on real IoT data. More specifically, we identify IoT data sources over the Web and develop a crawler engine to collect large-scale real-world IoT data for the first time. We make the results of our work available to the public in order to assist the community in the future research. In particular, we collect the data of nearly two million Internet connected objects and study trends in IoT using a real-world query set from an IoT search engine. Based on the collected data and our analysis, we identify the typical characteristics of IoT data. The most intriguing finding of our study is that IoT data is mainly disseminated using Web Mapping while the emerging IoT solutions such as the Web of Things, are currently not well adopted. On top of our findings, we further discuss future challenges and open research problems in the IoT area.