The Mr. DLib team, as part of the ADAPT Centre and Trinity College Dublin, has received funding to hire 2 employees for 2 years to spin-out a business start-up in the field of recommendations-as-a-service and machine learning. The two positions are to be filled with one software architect / product manager and one software engineer, […]
We released version 1.3 of Mr. DLib. The new major feature is “word embeddings” based recommendations. We are excited to see how the new recommendations will perform with our partners. In addition, we fixed many small bugs, and added some minor improvements. A complete overview can be found in JIRA.
The new version of Mr. DLib completes 104 issues. The most notable ones are: We improved the keyphrase extraction, i.e. keyphrases are no stored differently in Lucene. We expect a better recommendation effectiveness and are currently running an A/B test. More robust path encoding for search queries (special characters in a URL caused errors) Lucene’s eDismax function […]
There are two major news coming along with the new version of Mr. DLib’s Recommendation API. JabRef finally uses Mr. DLib for it’s recommender system We have announced this already a while ago, but now, finally, Mr. DLib’s recommendations are available in one of the most popular open-source reference managers, i.e. JabRef. Currently, Mr. DLib […]
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 […]
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 […]