搜广推&NLP03-顶会track记录

IR 会议相关

WSDM: Topics covered include but are not limited to:

• Web Search
• Adversarial search
• Algorithms and systems for Web-scale search
• Audio and touch interfaces to search
• Distributed search, metasearch, peer-to-peer search
• Indexing web content
• Local and mobile search
• Multimedia Web search
• Query analysis and query processing
• Search benchmarking and evaluation
• Search user behavior and log analysis
• Search user interfaces and interaction
• Searching social and real-time content
• Semantic search, faceted search, and knowledge graphs
• Sponsored search
• Task-driven search
• Vertical portals and search
• Voice search, conversational search, and dialog in search
• Web crawling
• Zero-query and implicit search
• Web Mining
• Algorithms and systems for Web-scale mining
• Clustering, classification, and summarization of Web data
• Data, entity, event, and relationship extraction
• Data extraction, integration and cleaning
• Discovery-driven Web and social network mining
• Geo and location data analysis
• Knowledge acquisition and automatic construction of knowledge bases
• Large-scale graph analysis
• Modeling trustworthiness and reliability of online information
• Multimodal data mining
• NLP for Web mining
• Online and streaming algorithms for Web data
• Opinion mining and sentiment analysis
• Web traffic and log analysis
• Web measurements, web evolution and web models
• Web recommender systems and algorithms
• Mobile Mining
• Neural architectures for Web search and mining
• Web search and data mining under privacy constraints
• Social Search, Mining and Other Applications
• Personal assistants, dialogue models, and conversational interaction
• Collaborative search and question answering
• Social network dynamics
• Human computation and crowdsourcing
• Influence and viral marketing in social networks
• Instant messaging and social networks
• Link prediction and community detection
• Location-based social networks
• Searching and mining crowd-generated data and collaboratively generated content
• Sampling, experiments, and evaluation in social networks
• Social media analysis: blogs and friendship networks
• Social network analysis, theories, models and applications
• Social reputation, influence, and trust
• Social tagging
• User activity modeling and exploitation
• Interpretable models of individual and social behavior

 

SIGIR:

• Search and Ranking:
• Queries and Query Analysis (e.g., query intent, query understanding, query suggestion and prediction, query representation and reformulation, spoken queries).
• Web Search (e.g., ranking at web scale, link analysis, sponsored search, search advertising, adversarial search and spam, vertical search).
• Retrieval Models and Ranking (e.g., ranking algorithms, learning to rank, language models, retrieval models, combining searches, diversity and aggregated search).
• Efficiency and Scalability (e.g., indexing, crawling, compression, search engine architecture, distributed search, metasearch, peer-to-peer search, search in the cloud).
• Foundations and Future Directions:
• New Theory (e.g., theoretical models, concepts and foundations of information retrieval and access).
• New Approaches (e.g., as part of a vision for important future IR scenarios).
• New Devices (e.g., consumer devices, wearable computing, neuroinformatics, sensors, Internet-of-Things, vehicles).
• Ethics, Economics, and Politics (e.g., studies on broader implications, norms and ethics, economic value, political impact).
• Perspectives (e.g., like keynotes with visionary reflections on a body of research, the field, theories, models, and methods, providing critical, provocative, creative ideas and insights, with actionable lessons for the near future).
• Domain-Specific Applications:
• Local and Mobile Search (e.g., location-based search, mobile usage understanding, mobile result presentation, audio and touch interfaces, geographic search, location context in search).
• Social Search (e.g., social networks in search, social media in search, blog and microblog search, forum search).
• Search in Structured Data (e.g., XML search, graph search, ranking in databases, desktop search, email search, entity-oriented search).
• Multimedia Search (e.g., image search, video search, speech and audio search, music search).
• Education (e.g,. search for educational support, peer matching, info seeking in online courses/MOOCs).
• Legal (e.g., e-discovery, patents, other applications in law).
• Health (e.g., medical, genomics, bioinformatics, other applications in health).
• Knowledge Graph Applications (e.g. conversational search, semantic search, entity search, KB question answering, knowledge-guided NLP, search and recommendation).
• Other Applications and Domains (e.g., digital libraries, enterprise, expert search, news search, app search, archival search, new retrieval problems including applications of search technology for social good).
• Content Recommendation, Analysis and Classification:
• Filtering and Recommending (e.g., content-based filtering, collaborative filtering, recommender systems, recommendation algorithms, zero-query and implicit search, personalized recommendation).
• Document Representation and Content Analysis (e.g., summarization, text representation, linguistic analysis, readability, NLP for search applications, cross- and multi-lingual search, information extraction, opinion mining and sentiment analysis, clustering, classification, topic models).
• Knowledge Aquisition (e.g. information extraction, relation extraction, event extraction, query understanding, human-in-the-loop knowledge aquisition)
• Artificial Intelligence, Semantics, and Dialog:
• Core AI (e.g. deep learning for IR, embeddings, intelligent personal assistants and agents).
• Question Answering (e.g., factoid and non-factoid question answering, interactive question answering, community-based question answering, question answering systems).
• Conversational Systems (e.g., conversational search interaction, dialog systems, spoken language interfaces, intelligent chat systems).
• Explicit Semantics (e.g. semantic search, named-entities, relation and event extraction).
• Knowledge Representation and Reasoning (e.g., link prediction, knowledge graph completion, query understanding, knowledge-guided query and document representation, ontology modeling).
• Ethics (e.g., algorithmic fairness, accountability, transparency, confidentiality, representativeness, discrimination and harmful bias).
• Human factors and interfaces:
• Mining and Modeling Users (e.g., user and task models, click models, log analysis, behavioral analysis, modeling and simulation of information interaction, attention modeling).
• Interactive Search (e.g., search interfaces, information access, exploratory search, search context, whole-session support, proactive search, personalized search).
• Social Search (e.g., social media search, social tagging, crowdsourcing).
• Collaborative Search (e.g., human-in-the-loop, knowledge aquisition).
• Information Security (e.g., privacy, surveillance, censorship, encryption, security).
• Evaluation:
• User-centered Evaluation (e.g., user experience and performance, user engagement and search task design).
• System-centered Evaluation (e.g., novel types of test collections, evaluation metrics).
• Beyond Cranfield (e.g., online evaluation, task-based, session-based, multi-turn, interactive search).
• Beyond Labels (e.g., simulation, implicit signals, eye-tracking and physiological approaches, such as fMRI).
• Beyond Effectiveness (e.g., usefulness, urgency, value, utility, credibility, authority, diversity).
• Methodology (e.g., statistical methods and reproducibility issues in IR evaluation).

CIKM:

• Data and information acquisition and preprocessing (e.g., data crawling, IoT data, data quality, data privacy, mitigating biases, data wrangling)
• Integration and aggregation (e.g., semantic processing, data provenance, data linkage, data fusion, knowledge graphs, data warehousing, privacy and security, modeling, information credibility)
• Efficient data processing (e.g., serverless, data-intensive computing, database systems, indexing and compression, architectures, distributed data systems, dataspaces, customized hardware)
• Special data processing (e.g., multilingual text, sequential, stream, spatio-temporal, (knowledge) graph, multimedia, scientific, and social media data)
• Analytics and machine learning (e.g., OLAP, data mining, machine learning and AI, scalable analysis algorithms, algorithmic biases, event detection and tracking, understanding, interpretability)
• Neural Information and knowledge processing (e.g., graph neural networks, domain adaptation, transfer learning, network architectures, neural ranking, neural recommendation, and neural prediction)
• Information access and retrieval (e.g., ad hoc and web search, facets and entities, question answering and dialogue systems, retrieval models, query processing, personalization, recommender and filtering systems)
• Users and interfaces for information and data systems (e.g., user behavior analysis, user interface design, perception of biases, personalization, interactive information retrieval, interactive analysis, spoken interfaces)
• Evaluation, performance studies, and benchmarks (e.g., online and offline evaluation, best practices)
• Crowdsourcing (e.g. task assignment, worker reliability, optimization, trustworthiness, transparency, best practices)
• Understanding multi-modal content (e.g., natural language processing, speech recognition, computer vision, content understanding, knowledge extraction, knowledge graphs, and knowledge representations)
• Data presentation (e.g., visualization, summarization, readability, VR, speech input/output)
• Applications (e.g., urban systems, biomedical and health informatics, legal informatics, crisis informatics, computational social science, data-enabled discovery, social media)

KDD:

• Data Cleaning and Preparation: A significant part of the data science lifecycle is spent on data cleaning and preparation. In several domains, data cleaning tasks continue to be rule-based and are often brittle, i.e., they break down in face of a constantly changing and evolving environment. Learning-based approaches for data cleaning and preparation which are generalizable and adaptive across domains are highly sought.

• Data Transformation and Integration: The process of mapping data from one representation into another is at the heart of data science. The mapping can be query driven, based on a statistical task, or might involve integrating data from myriad sources. We seek original contributions that address the trade-off between the complexity of the transformation and algorithmic efficiency.

• Mining, Inference, and Learning: These topics are the kernel of knowledge discovery from databases (KDD) paradigm and continue to witness massive growth. While classical aspects of supervised learning have been mainstreamed into the development cycle, new variations on unsupervised learning like self-supervision, few shot learning, prescriptive learning (reinforcement learning), transfer learning, meta learning, and representational learning are pushing the research boundary in a world where the proportion of labeled and annotated data is becoming minuscule. In each of these topics, we seek submissions that highlight the trade-off between accuracy, stability, robustness, and efficiency. Submissions that propose “new” inference tasks are strongly encouraged.

• Explainability: As data science models are becoming part of daily human activity there is a need, often being expressed in law, that the models be fair, interpretable, and provide mechanisms to explain how a prediction or decision by the model was arrived at. Interpretable models will lead to their wider acceptance in society at large and increase the value of Data Science as a discipline in its own right.

• Data Privacy and Ethics: Data privacy or lack thereof, continues to be the achilles heel of the whole data science enterprise. We seek technical contributions that advance the state of data science methods while guaranteeing individual privacy, respect for societal norms and ethical integrity.

• Model Dissemination: Migrating a data science model from a research lab to a real-world deployment is non-trivial and potentially a continuous ongoing process. We seek research submissions that highlight and address technical and behavioral challenges during model deployment, feedback, and upgradation.

 

WWW: Track Search Track:

• Web Search Models and Ranking
• Personalized and Context-aware Search and Ranking
• Query Analysis
• Web Crawling and Indexing
• Search Engine Architectures and Scalability
• Evaluation Methodologies and Metrics
• Interactive Search and Result Presentation
• Domain Specific Search
• New Search Paradigms (e.g. Task-based Search, Conversational Search, Zero-query Search)
• Search on Non-traditional Devices (e.g. Wearable Computing, Neuroinformatics, Sensors, IoT, Vehicles)
• Semantic Search
• Text Mining for Search
• Multilingual and Cross-lingual Search
• Fairness, Transparency, Ethics and Bias in Web Search
  

ECIR:

ACL: 

EMNLP:

IJCAI:

AAAI:

NAACL:

ICLR: