Interactive Machine Learning and Semantic Information Retrieval

Retrieval techniques operating on text or semantic annotations have become the industry standard for retrieval from large document collections. However, traditional information retrieval techniques operate on the assumption that the user issues a single query and the system responds with a ranked list of documents. In recent years we have witnessed a substantial growth in text data coming from various online resources, such as online newspapers, blogs, specialised document collections (e.g. arXiv). Traditional information retrieval approaches often fail to provide users with adequate support when browsing such online resources, hence in recent years there has been a growing interest in developing new algorithms and design methods that can support interactive information retrieval. The aim of this workshop is to explore new methods and related system design for interactive data analytics and management in various domains, including specialised text collections (e.g. legal, medical, scientific) as well as for various tasks, such as semantic information retrieval, conceptual organization and clustering of data collections for sense making, semantic expert profiling, and document recommender systems.
Of interest, also, is probabilistic and machine learning formulations of the interactive information retrieval task above and beyond the simple "stochastic language models" framework developed in the information retrieval community.
The primary audience of the workshop are researchers and practitioners in the area of interactive and personalised system design as well as interactive machine learning both from academia and industry.