8th International Workshop on Information Filtering and Retrieval - DART 2014

Workshop of the XIII AI*IA Conference Pisa, 10 December 2014

Website: http://aiia2014.di.unipi.it/dart/index


  • Submission deadline: September 15, 2014
  • Notification of acceptance: October 15, 2014
  • Final Paper Submission and Registration: October 31, 2014
  • Workshop day: December 10, 2014

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With the increasing availability of data, it becomes more important to have automatic methods to manage data and retrieve information. Data processing, especially in the era of Social Media, is changing users behaviors. Users are ever more interested in information rather than in mere raw data. Considering that the large amount of accessible data sources is growing, novel systems providing effective means of searching and retrieving information are required. Therefore the fundamental goal is making information exploitable by humans and machines.

DART 2014 intends to provide a more interactive and focused platform for researchers and practitioners for presenting and discussing new and emerging ideas. It is focused on researching and studying new challenges in intelligent information filtering and retrieval. In particular, DART aims to investigate novel systems and tools to web scenarios and semantic computing. In so doing, DART will contribute to discuss and compare suitable novel solutions based on intelligent techniques and applied in real-world applications.

Information Retrieval attempts to address similar filtering and ranking problems for pieces of information such as links, pages, and documents. Information Retrieval systems generally focus on the development of global retrieval techniques, often neglecting individual user needs and preferences.

Information Filtering has drastically changed the way information seekers find what they are searching for. In fact, they effectively prune large information spaces and help users in selecting items that best meet their needs, interests, preferences, and tastes. These systems rely strongly on the use of various machine learning tools and algorithms for learning how to rank items and predict user evaluation.


Topics of interest will include (but not are limited to):

  • Web Information Filtering and Retrieval
  • Web Personalization and Recommendation
  • Web Advertising
  • Web Agents
  • Web of Data
  • Semantic Web
  • Open Data
  • Linked Data
  • Semantics and Ontology Engineering
  • Search for Social Networks and Social Media
  • Natural Language and Information Retrieval in the Social Web
  • Real-time Search
  • Text categorization