HFIR 2012 - Special Session on Human Factors in Information Retrieval

Special Session on Human Factors in Information Retrieval
Through numerous research programs, competitions, and economic surveys, automated information retrieval systems and recommender systems have been proven to be ef?cient and useful by reducing the cognitive load and time required during the search and access to data. Over the past two decades of research within this field, this improvement of human-computer interactions is mainly relying on increasing systems' accuracy at different levels. Usage mining techniques aim at inferring accurate preferences, habits, and interests and building profiles from users' actions. Collaborative and content-based filtering make use of these profiles to provide users with relevant recommendations.
Ontology-based systems formally define concepts within a domain, thus reducing ambiguity. All these machine learning models and algorithms are evaluated relatively to true risk and empirical risk, leading to very accurate contents. Yet, a crucial aspect is missing within these evaluation metrics. It does not take into account human factors playing a role within the decision process. Even the most relevant information is not sufficient to maximize users' acceptance/adoption rate, and satisfaction. The time has come to design holistic intelligent systems that provide the right information at the right time, in the correct manner, in agreement with users' policy and with valuable arguments. New challenges consist in: (1) identifying human factors that play a role within decision making an/or maximize users' acceptance, adoption and satisfaction, (2) integrating these factors in machine learning algorithms, (3) designing interfaces to improve human-computer interactions.
Topics of interest include (but are not limited to) the following:
Human factors and decision making (diversity, personality, emotions, mood, culture, ...)
Preserving privacy while modeling users and recommending items
Trust and reputation
Context and information retrieval
Impact of recommenders on decision process
Multi-criteria optimization (privacy vs. accuracy, diversity vs. similarity, scalability vs. time constraints, ...)
User studies (identifying human factors, evaluation of recommender systems)
Social influence (leaders, explicit and implicit social networks, maximizing acceptance, manipulation...)
User-centered design and adaptation of interfaces
Presentation and explanations in recommender systems
Visual representation of data
Important Dates
Electronic submission of full papers: June 1, 2012
Notification of paper acceptance: August 1, 2012
Camera-ready of accepted papers: August 31, 2012
Conference: December 4-7, 2012
Submission Guidelines
Authors are invited to submit their manuscript in LNCS/LNAI style (please see this page), maximum 10 pages.
Paper should be submitted in PDF form via ISMIS 2012 Online Submission System:
http://wi-lab.com/cyberchair/2012/ismis12/cbc_inde...
The submitted papers will be reviewed by the session's program committee