Recommender Systems using Pennant Diagrams in Digital Libraries

Introduction Recommender systems in search systems are an established way of pointing the user to related content. Commercial companies like Amazon have been using recommendations for a while by showing the user products related to their current search context or usage behaviour. In digital libraries recommendations can be valuable for researchers, e.g. recommending related literature to a given context. Typically, in a scientific context the simple presentation of related content is not sufficient. Often the users demand a more detailed view on the connection of a document and its specific recommendations. Key questions are: What is mostly related to the current document? Who are the main researchers in a particular area? Which research topics are related to the current document? One way of visualizing the relatedness / distance between a given document and it’s recommendations are the so-called pennant diagrams. The aim of pennants is to provide the user with a graph showing the relatedness / distance between a given document and related documents. Co-citation but also co-occurrence analysis are established methods for finding related documents to a seed. A seed could be for instance an author, a keyword, or a publication. However, these approaches are not suitable to make any assumption about the distance between the seed and its co-mentioned documents. In this paper we introduce a recommender system in the digital library sowiport using pennant diagrams which can be created from co-citation and/or co-occurrence analysis.