会议全称：ACM International Conference on Information and Knowledge Management
We encourage submissions of high-quality research papers on the general areas of artificial intelligence, data science, databases, information retrieval, and knowledge management. Topics of interest include, but are not limited to, the following areas:
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Data and information acquisition and preprocessing (e.g., data crawling, IoT data, data quality, data privacy, mitigating biases, data wrangling)
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Integration and aggregation (e.g., semantic processing, data provenance, data linkage, data fusion, knowledge graphs, data warehousing, data lake, privacy and security, modeling, information credibility)
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Efficient data processing (e.g., serverless, data-intensive computing, database systems, indexing and compression, architectures, distributed data systems, dataspaces, customized hardware)
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Special data processing (e.g., multilingual text, sequential, stream, time series, spatio-temporal, (knowledge) graph, multimedia, scientific, and social media data)
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Analytics and machine learning (e.g., OLAP, data mining, machine learning and AI, scalable analysis algorithms, algorithmic biases, event detection, and tracking, understanding, and interpretability)
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Neural Information and knowledge processing (e.g., graph neural networks, domain adaptation, transfer learning, network architectures, neural ranking, neural recommendation, and neural prediction)
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Data preparation, Valuation, and Trading
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Information access and retrieval (e.g., web search, question answering and dialogue systems, retrieval models, query processing, personalization, recommender, and filtering systems)
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Users and interfaces for information systems (e.g., user behavior analysis, user interface design, perception of biases, personalization, interactive information retrieval, interactive analysis, spoken interfaces)
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Evaluation, performance studies, and benchmarks (e.g., online and offline evaluation, best practices)
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Crowdsourcing (e.g. task assignment, worker reliability, optimization, trustworthiness, transparency, best practices)
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Mining multi-modal content (e.g., natural language processing, speech recognition, computer vision, content understanding, knowledge extraction, knowledge graphs, and knowledge representations)
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Data presentation (e.g., visualization, summarization, readability, VR, speech input/output)
