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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 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A Study on Preference Intensity and Temporal Context From Limited Labels to Open Domains:An Efficient Learning Method for Drone-view Geo-Localization User Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data
Klout Topics for Modeling Interests and Expertise of Users Across Social Networks
Sarah Ellinger, Prantik Bhattacharyya, Preeti Bhargava, Nemanja · 2017-10-27 · via cs.IR updates on arXiv.org

This paper presents Klout Topics, a lightweight ontology to describe social media users' topics of interest and expertise. Klout Topics is designed to: be human-readable and consumer-friendly; cover multiple domains of knowledge in depth; and promote data extensibility via knowledge base entities. We discuss why this ontology is well-suited for text labeling and interest modeling applications, and how it compares to available alternatives. We show its coverage against common social media interest sets, and examples of how it is used to model the interests of over 780M social media users on Klout.com. Finally, we open the ontology for external use.

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