Mr. DLib: Recommender-System As-a-Service (Recommender Systems Dublin)

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 Read more…

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 Read more…

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 Read more…

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 Read more…