Jobs / Career

We Are Hiring: 1 Software/Machine-Learning Engineer & 1 Software Architect / Product Owner for a Recommender-System Business Start-up

UPDATE: We will soon advertise another position for this start-up. Please come back in a few days. The School of Computer Science and Statistics of Trinity College Dublin and the ADAPT Centre received funding to hire 2 employees for 2 years* to spin-out a business start-up in the field of recommender-systems as-a-service and machine learning in Dublin. The two positions are to be filled with one machine-learning engineer and one software architect/product manager, whereas both employees are expected to work together very closely. They will be responsible for developing a recommender-system as-a-service that uses a unique technology, based on the research of Prof Dr Joeran Beel who will be the project lead (read here for a brief outline of the Read more…

By Joeran Beel, ago
Mr. DLib

Mr. DLib Recommendations-as-a-Service v1.3: “Word Embeddings” and Many Minor Improvements and Bug Fixes

We released version 1.3 of Mr. DLib´s Recommender-System as-a-Service. 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.

By Joeran Beel, ago
Mr. DLib

Mr. DLib v1.2.1: Improved keyphrase recommendations and Apache Lucene query handling

The new version of our recommender system completes 104 issues and significantly improves the recommendations. The most notable improvements are: We improved the keyphrase extraction process in the recommender system, i.e. keyphrases are not stored differently in Lucene. We expect 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 is A/B tested (together with Lucene’s standard query parser) Improved queries for CORE recommender system (their system needs queries to be of a certain length; Mr. DLib now just multiplies the queries until they are at least 50 characters) Abstracts and keywords in the XML response of Mr. DLib are enclosed in <![CDATA[ HTML Snippet is improved Read more…

By Joeran Beel, ago
Mr. DLib

Mr. DLib 1.2 released: JabRef recommendations completed; CORE recommendation API connected

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 enables JabRef users to retrieve a list of related-article recommendations, given a currently selected entry in the reference list (see screenshot). In the long run, we aim for creating personalized recommendations, too. Mr. DLib is not the only provider of recommendations-as-a-service in Academia. Another provider is the CORE project, with whom we partnered now. CORE is offering an API similar to the one we offer. We Read more…

By Joeran Beel, ago
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
Recommendations as-a-Service (RaaS)

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 is shown. Using the JavaScript also means that the logging will be more reliable because web spiders are not logged any more. Our partner Sowiport uses the JavaScript already. We indexed 15 million documents from CORE and recommend them through our API. Another 5 million will follow soon. So far, recommendations could only be requested by specifying a particular document ID such as https://api-beta.mr-dlib.org/v1/documents/<ID>/related_documents/. Now, recommendations can Read more…

By Joeran Beel, ago
Docear

Docear’s Online Services Are Down (Recommendation; User Registration; Backup)

Currently, all of Docear’s online services are down, including the recommender system. This means, you cannot register, log-in to download backups, or receive recommendations. As we have no time right now for the development of Docear, we are afraid that we won’t be able to fix this problem anytime soon. However, we adjusted the current version of Docear (v1.2) to dynamically deal with this situation. This means, as long as the services are down, the recommendation button is not shown and the registration dialog is not shown when installing Docear for the first time. As soon as the services are up again, all the buttons etc. are automatically shown again. Update 2017-07-20: Probably, we will not activate the current recommender system again Read more…

By Joeran Beel, ago
Publications

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 have proven their effectiveness. We were interested to find out how stereotype and most-popular recommendations would perform in the scenario of a digital library. Therefore, we implemented the two approaches in the recommender system of GESIS’ digital library Sowiport, in cooperation with the recommendations-as-a-service provider Mr. DLib. We measured the effectiveness of most-popular and stereotype recommendations with click-through rate (CTR) based on 28 million delivered 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