About Dart 2017

Workshop of the 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017)

Technological evolution in electronic and computer science has led to the advent of the so called information age. Information is continuously created and stored at an impressive pace. Nowadays, data is readily available in large quantities and on an enormous variety of subjects. It becomes more important to have automatic methods to manage and indexing data, in order to search and retrieve only useful information. Advanced information retrieval methods, tools, and infrastructures require expertise in different research areas, including machine learning, data mining, computer linguistics, artificial intelligence, user interaction and modeling, Web engineering, or distributed systems.

DART 2017  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.