WSDM 2020 : ACM International Conference on Web Search and Data Mining

  in Conferences   Posted on June 1, 2019

Conference Information

Submission Deadline Friday 16 Aug 2019 Proceedings indexed by :
Conference Dates Feb 5, 2020 - Feb 9, 2020
Conference Address Houston, United States
Conference & Submission Link http://www.wsdm-conference.org/2020/
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

Guide2Research Overall Ranking: 34
Category Rankings
Machine Learning & Arti. Intelligence 13
Web, Mobile & Multimedia Technologies 4

Google Scholar H5-index:
55

Conference Call for Papers

WSDM (pronounced \”wisdom\”) is one of the premier conferences on web-inspired research involving search and data mining. The 13th ACM International WSDM Conference will take place in Houston, Texas from February 5-9, 2020.

WSDM is a highly selective conference that includes invited talks, as well as refereed full papers. WSDM publishes original, high-quality papers related to search and data mining on the Web and the Social Web, with an emphasis on practical yet principled novel models of search and data mining, algorithm design and analysis, economic implications, and in-depth experimental analysis of accuracy and performance.

Original papers emphasizing novel algorithmic approaches are particularly encouraged. Application-oriented papers that make innovative technical contributions to research are welcome. Visionary papers on new and emerging topics are also welcome.

List of Topics

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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

Authors are explicitly discouraged from submitting papers that do not clearly present their contribution with respect to previous works, that contain only incremental results, and that do not provide significant advances over existing approaches.

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