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An open challenge in information distillation is the evaluation and optimization of the utility of ranked lists with respect to flexible user interactions over multiple sessions. Utility depends on both the relevance and novelty of documents, and the novelty in turn depends on the user interaction history. However, user behavior is non-deterministic. We(More)
In this paper, the main area of concentration was to optimize the rules generated by Association Rule Mining (apriori method), using Genetic Algorithms. In general the rule generated by Association Rule Mining technique do not consider the negative occurrences of attributes in them, but by using Genetic Algorithms (GAs) over these rules the system can(More)
This paper examines a new approach to information distillation over temporally ordered documents, and proposes a novel evaluation scheme for such a framework. It combines the strengths of and extends beyond conventional adaptive filtering, novelty detection and non-redundant passage ranking with respect to long-lasting information needs ("tasks" with(More)
Email is one of the most prevalent communication tools today, and solving the email overload problem is pressingly urgent. A good way to alleviate email overload is to automatically prioritize received messages f1ording to the priorities of each user. However, research on statistical learning methods for fully personalized email prioritization has been(More)
In many practical applications, multiple interrelated tasks must be accomplished in sequential order through user interactions with multiple retrieval, classification and recommendation systems. The ordering of the tasks may have a significant impact on the overall utility (or performance); hence optimal ordering of tasks is desirable. However, manual(More)