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 […]
On 28th February, we released version 1.1.1 with some minor improvements and bug fixes: Improved 404 error handling for unkown document IDs Fix: The order of authors in the XML was not sorted properly Several internal changes (adjusted logging table; click time is not updates any more for second click etc; automatic tool to add stereotype […]
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 […]
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 […]
We have integrated several new recommendation algorithms into Mr. DLib. Some 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 algorithms: Random The approach randomly picks the set of documents to recommend to the user. We experiment with this […]
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 servers, one for the development, one for beta, and […]
We are proud to announce that the social science portal Sowiport is using Mr. DLib as first pilot partner. Sowiport pools and links quality information from domestic and international providers, making it available in one place. Sowiport currently contains 9.5 million references on publications and research projects. The documents in Sowiport comprise bibliographic metadata (such […]