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
Machine Learning

Mr. DLib v1.1.1 released: minor improvements

On 28th February, we released version 1.1.1 of Mr. DLib’s recommender system with some minor improvements and bug fixes: Improved 404 error handling for unknown document IDs Fix: The order of authors in the XML was not sorted properly Several internal changes (adjusted logging table; click time is not updated any more for second clicks etc;an automatic tool to add stereotype recommendations)

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
Recommendations as-a-Service (RaaS)

Two new RaaS servers are online (dev and beta system)

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 recommender-system servers, one for the development, one for beta, and one for production.

By Joeran Beel, ago
Docear

Docear 1.0.3 Beta: rate recommendation, new web interface, bug fixes, …

Update: February 18, 2014: No bugs were reported, as such we declare Docear 1.03 with its recommender system as stable. It can be downloaded on the normal download page.


With Docear 1.0.3 beta we have improved PDF handling, the recommender system, provided some help for new users and enhanced the way how you can access your mind maps online. PDF Handling We fixed several minor bugs with regard to PDF handling. In previous versions of Docear, nested PDF bookmarks were imported twice when you drag & dropped a PDF file to the mind map. Renaming PDF files from within Docear changed the file links in your mind maps but did not change them in your BibTeX file. Both issues are fixed now. To rename a PDF file from within Docear you just have to right-click it in Docear's workspace panel on the left hand side and it is important that the mind maps you have linked the file in, are opened. We know, this is still not ideal, and will improve this in future versions of Docear. Rate Your Recommendations

You already know about our recommender system for academic literature. If you want to help us improving it, you can now rate how good a specific set of recommendations reflects your personal field of interest. Btw. it would be nice if you do not rate a set of recommendations negatively only because it contains some recommendations you received previously. Currently, we have no mechanism to detect duplicate recommendations.

rate a literature recommendation set

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By Joeran Beel, ago