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The scientific literature has played an important role in the dissemination of new knowledge throughout the past century. However, the increasing numbers of scientific articles being published in recent years has intensified the perception of information overload for users attempting to find relevant scientific information. The purpose of this paper is to describe a task‐focused strategy that employs the task profiles of users to make recommendations in a digital library.

This paper combines information retrieval, common citation analysis, and coauthor relationship analysis techniques with a citation network analysis technique – the CiteRank algorithm – to find relevant and high‐quality articles. In total, nine variations of the proposed approach were tested using articles downloaded from the CiteSeerX system and usage logs collected from the authors' experimental server.

The results from the authors' experimental evaluations demonstrate that the proposed Content‐citation approach outperforms the Relevance‐CiteRank, Relevance‐citation count, and Relevance‐only approaches.

This paper describes an original study that has produced a novel way to combine information retrieval, common citation analysis, and coauthor relationship analysis techniques to find relevant and high‐quality articles for recommendation in a digital library.

The dynamic measure of risk problem in a incomplete market is discussed when stock appreciation rates are uncertain. Meanwhile, a related stochastic game problem is studied. The value of a stochastic optimal control is regarded as a reasonable measure of the risk. The form of the optimal objective is obtained by employing the tools of BSDE theory.