Web analytics of user path tracing and a novel algorithm for generating recommendations in Open Journal Systems

The use of articles from scientific journals is an important part of research-based teaching at universities. The selection of relevant work from among the increasing amount of scientific literature can be problematic; the challenge is to find relevant recommendations, especially when the related articles are not obviously linked. This paper seeks to discuss these issues.

This paper focuses on the analysis of user activity traces in journals using the open source software “Open Journal Systems” (OJS). The research questions to what extent end users follow a certain link structure given within OJS or immediately select the articles according to their interests. In the latter case, the recorded data sets are used for creating further recommendations. The analysis is based on an article matrix, displaying the usage frequency of articles and their user selected successive articles within the OJS. Furthermore, the navigation paths are analysed.

It was found that the users tend to follow a set navigation structure. Moreover, a hybrid recommendation system for OJS is described, which uses content based filtering as the basic system extended by the results of a collaborative filtering approach.

The paper presents two original contributions: the analysis of user path tracing and a novel algorithm that allows smooth integration of new articles into the existing recommendations, due to the fact that scientific journals are published in a frequent and regular time sequence.