ProAct 2016 - ProActive Information Retrieval: Anticipating Users' Information Needs (ProAct IR)

The ultimate goal of an IR system is to fulfill the user’s information need. Traditional search systems have been reactive in nature wherein the search systems react to an input query and return a set of ranked documents most probable to contain the desired information. Due to the inability of, and efforts required by users to create efficient queries expressing their information needs, techniques such as query expansion, query suggestions, using relevance feedback and click-through information, personalization, etc. have been used to better understand and satisfy users’ information needs. Given the increasing popularity of smart-phones and Internet enabled wearable devices, how can the information retrieval systems use the additional data, and better interact with the user so as to better understand, and even anticipate her precise information needs? Moving towards such Zero Query Minimum User Effort systems require research efforts from multiple disciplines covering algorithmic aspects of retrieval models, user modeling and profiling, evaluation, context modeling, novel user interfaces design, etc. The proposed workshop intends to gather together the researchers from academia and industry practitioners with these diverse backgrounds to share their experiences and opinions on challenges and possibilities of developing such proactive information retrieval systems.
The prime objective of the workshop will be to attract attention of the IR community to, and spark discussions about, systems that can proactively anticipate and fulfill the information needs of the user. Building such systems require research efforts from various areas covering algorithmic aspects of retrieval models, user modeling and profiling, evaluation, context modeling, etc. It also requires research efforts in UI design, result presentation and visualization. In order to achieve this goal, the workshop will strive to:
provide a platform to researchers from academia and industry to share latest research, discuss current shortcomings, explore different use cases;
brainstorm about future research directions towards developing systems that can intelligently anticipate users’ information needs;
build upon the current research in information retrieval, natural language processing, semantic analysis, personalization, etc.;
identify killer applications and key industry drivers (bringing theory into practice);
explore means of developing benchmark test collections and evaluation metrics for evaluation.