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.  Here is the list: 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 […]

Some numbers about Mr. DLib’s Recommendations-as-a-Service (RaaS)

Six month ago, we launched Mr. DLib’s recommendations-as-a-service for Academia. Time, to look back and provide some numbers: Since September 2016, Mr. DLib has delivered 60,836,800 recommendations to our partner Sowiport, and Sowiport’s visitors users have clicked 91,545 of the recommendations. This equals on overall click-through rate (CTR) of 0.15%. The figure shows the number […]

Mr. DLib v1.1 released: JavaScript Client, 15 million CORE documents, new URL for recommendations-as-a-service via title search

We are proud to announce version 1.1 of Mr. DLib’s Recommender-System as-a-Service. The major new features are: A JavaScript Client to request recommendations from Mr. DLib. The JavaScript offers many advantages compared to a server-side processing of our recommendations. Among others, the main page will load faster while recommendations are requested in the background and a loading animation […]

Paper accepted at ISI conference in Berlin: “Stereotype and Most-Popular Recommendations in the Digital Library Sowiport”

Our paper titled “Stereotype and Most-Popular Recommendations in the Digital Library Sowiport” is accepted for publication at the 15th International Symposium on Information Science (ISI) in Berlin. Abstract: Stereotype and most-popular recommendations are widely neglected in the research-paper recommender-system and digital-library community. In other domains such as movie recommendations and hotel search, however, these recommendation approaches […]

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 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 […]

Enhanced re-ranking of our recommendations based on Mendeley’s readership statistics

Content-based filtering suffers from the problem that no human quality assessments are taken into account. This means, a poorly written paper ppoor would be considered equally relevant for a given input paper pinput as high-quality paper pquality if pquality and ppoor contain the same words. We elevate for this problem by using Mendeley’s readership data for re-ranking Mr. DLib’s […]