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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.

Web content has been widely used for recommending personal webpages. Despite its popularity, the content‐based approach regards a webpage simply as a piece of text, thereby often resulting in less authoritative recommendations of webpages. This paper aims to propose novel approaches that utilise other sources of information pertaining to webpages to facilitate the automatic construction of an authoritative web recommender system.

In this research, four approaches that exploit hyperlink structure, web content and web‐usage logs for making recommendations are proposed. The proposed approaches have been implemented as a prototype system, called the authoritative web recommender (AWR) system. An evaluation using the web‐usage logs and the corresponding pages of a university web site was performed.

The results from the evaluations using empirical data demonstrate that the four proposed approaches outperform the traditional content‐only approach.

This paper describes a novel way to combine information retrieval, usage mining and hyperlink structure analysis techniques to find relevant and authoritative webpages for recommendation.

This article describes a service for providing literature recommendations, which is part of a networked digital library project whose principal goal is to develop technologies for supporting digital services. The proposed literature recommendation system makes use of the Web usage logs of a literature digital library. The recommendation framework consists of three sequential steps: data preparation of the Web usage log, discovery of article associations, and article recommendations. We discuss several design alternatives for conducting these steps. These alternatives are evaluated using the Web logs of our university’s electronic thesis and dissertation (ETD) system. The proposed literature recommendation system has been incorporated into our university’s ETD system, and is currently operational.