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
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
Recommendations as-a-Service (RaaS)

Enhanced re-ranking in our recommender system based on Mendeley’s readership statistics

Content-based filtering recommendations suffer 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 recommendations. This means, once we have a number of e.g. 20 documents that are related for a requested input paper, we re-rank the 20 documents based on the number of readers they have on Mendeley. The most read papers are then recommended. More details will follow.

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

New recommendation algorithms integrated to Mr. DLib’s recommender system

We have integrated several new recommendation algorithms into Mr. DLib. Some recommendation 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 recommendation algorithms in its recommender system: Random Recommendations The approach recommendation randomly picks the set of documents to recommend. We experiment with this approach by randomly choosing to apply a language filter 50% of the time. With the language filter, the recommended documents share the same language as the input document. Lucene’s More Like This This is one of the most commonly applied recommendation aproaches for content-based filtering. The approach concatenates and tokenizes a document’s title, abstract, keywords, and journal name using Apache Lucene’s 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