In this section we introduce prototypes that were developed in research projects that are related to EconBiz.
Scientific Paper Recommendation using Sparse Title Data developed in the EU project MOVING (http://moving-project.eu/).
This prototype can be used to get recommendations for publications that may be of interest based on the user’s Twitter profile. In a first step, the most important terms in the Twitter stream are identified. This experiment only works with a public Twitter account name. The system delivers papers in economics and business based on the tweets of the user and the most relevant matching terms. It profiles papers as well as tweets using the novel method HCF-IDF (Hierarchical Concept Frequency Inverse Document Frequency). HCF-IDF extracts semantic concepts from texts and applies spreading activation based on a hierarchical thesaurus, which is freely available in many different domains. Spreading activation enables to extract relevant semantic concepts which are not mentioned in texts and mitigates shortness and sparseness of texts. The novel method HCF-IDF demonstrated the best performance in a larger user experiment published at JCDL 16 (http://dx.doi.org/10.1145/2910896.2910898). In this demo, you may compare the two different configurations, HCF-IDF using only titles of papers and HCF-IDF using both titles and full-texts of papers. Different from the traditional methods, HCF-IDF can provide competitive recommendations already using only titles.