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 […]
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 […]
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 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 […]
A demonstration paper about the integration of Mr. DLib in JabRef is accepted for publication at ECIR 2017. We will update this post soon with more information and a pre-print.
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 […]
We are glad to announce that JabRef – one of the most popular open-source reference manager – will soon be using Mr. DLib to provide recommendatins to its users. The team of Mr. DLib and JabRef had several Skype calls, and we finally agreed about a concept how to integrate Mr. DLib into JabRef. In a first […]
Our servers are now monitored by UptimeRobot, a free monitoring service. You can access all our server statuses at this URL https://stats.uptimerobot.com/WLL5PUjN6 and you will see a dashboard like this: A click on one of the server names will show you more details, e.g. https://stats.uptimerobot.com/WLL5PUjN6/778037437
We have integrated several new recommendation algorithms into Mr. DLib. Some algorithms are only ought as baselines for our researchers, others hopefully will further increase the effectiveness of Mr. DLib. Overall, Mr. DLib now uses the following algorithms: Random The approach randomly picks the set of documents to recommend to the user. We experiment with this […]
So far, Mr. DLib’s recommender system was running only on a single server. Consequently, when me messed up something in the development environment, sometimes the production system was affected, i.e. down. From today on, we have two additional dedicated servers running, meaning we have a total of three servers, one for the development, one for beta, and […]