Mr. DLib

Several new publications: Mr. DLib, Lessons Learned, Choice Overload, Bibliometrics (Mendeley Readership Statistics), Apache Lucene, CC-IDF, TF-IDuF

In the past few weeks, we published (or received acceptance notices for) a number of papers related to Mr. DLib, research-paper recommender systems, and recommendations-as-a-service. Many of them were written during our time at the NII or in collaboration with the NII. Here is the list of publications: Beel, Joeran, Bela Gipp, and Akiko Aizawa. “Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia.” In Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), 2017. Beel, Joeran. “Real-World Recommender Systems for Academia: The Gain and Pain in Developing, Operating, and Researching them.” In 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR) at the 39th European Conference on Information Retrieval (ECIR), 2017. [short version, official], [long version, arxiv] Beierle, Felix, Akiko Aizawa, and Joeran Beel. Read more…

By Joeran Beel, ago
Machine Learning

Two of our papers about citation and term-weighting schemes got accepted at iConference 2017

Two of our papers about weighting citations and terms in the context of user modeling and recommender systems got accepted at the iConference 2017. Here are the abstracts, and links to the pre-print versions: Evaluating the CC-IDF citation-weighting scheme: How effectively can ‘Inverse Document Frequency’ (IDF) be applied to references? In the domain of academic search engines and research-paper recommender systems, CC-IDF is a common citation-weighting scheme that is used to calculate semantic relatedness between documents. CC-IDF adopts the principles of the popular term-weighting scheme TF-IDF and assumes that if a rare academic citation is shared by two documents then this occurrence should receive a higher weight than if the citation is shared among a large number of documents. Although CC-IDF Read more…

By Joeran Beel, ago