DIVA represents user preferences using pairwise comparisons among items, rather than numeric ratings, and uses a novel similarity measure based on the concept of the probability of conflict between two orderings of items.
The DIVA (DecisionTheoretic Interactive Video Advisor) system elicits user preferences using a case-based technique and hard constraints are used to permit the user to communicate temporary deviations from his basic preferences.
A comprehensive methodology for evaluating a user model is described and used to assess the effectiveness of an adaptive user model embedded in a medical information retrieval, demonstrating that the user model helps to improve the retrieval quality without degrading the system performance.
DIVA is described, a decision-theoretic agent for recommending movies that contains a number of novel features and has a rich representation of preference, distinguishing between a user's general taste in movies and his immediate interests.
This paper proposes a novel methodology that detects malicious analysts who attempt to manipulate decision makers' perceptions through their intelligence reports by employing a user-modeling technique that automatically builds a computational model of each analyst based on observation of their activities.
The results of this paper show that different users have different assessments with regard to information coverage and the way that information is presented in both loosely and closely related document sets.
This model uses the Commander's past behaviors and generalize Commander's actions across multiple problems and multiple decision making sequences in order to recommend actions to a Commander in a manner that he may have taken and constructs a set of scenarios in the game where rational, intuitive and spontaneous decision making styles will be evaluated.
The goal is to provide a dynamic user model of an analyst and work with him as he goes about his daily tasks and predict the goals and intentions of the analyst in order to better serve their information seeking tasks.
A user modeling technique is employed to model an insider based on logged information and documents accessed while accomplishing an intelligence analysis task and creates a computational model for each insider and applies several detection metrics to analyze this model as it changes over time.
A dynamic user model to predict the analyst’s intent and help the information retrieval application better serve the analyst's information needs is developed and shown to help retrieve more relevant documents than the Verity Query Language system.