Machine Learning

A Novel Approach to Recommendation Algorithm Selection using Meta-Learning

Our paper “A Novel Approach to Recommendation Algorithm Selection using Meta-Learning” was accepted for publication at the 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS): Introduction  The ‘algorithm selection problem’ describes the challenge of finding the most effective algorithm for a given recommendation scenario. Some typical recommendation scenarios are news websites [3], digital libraries [4, 5], movie-streaming platforms [13]. The performance of recommender system algorithms vary in these different scenarios [3, 6, 10, 11, 15] as illustrated in Fig. 1. Performance variation occurs for many reasons, for example, the effectiveness of collaborative filtering algorithms changes depending on the number of ratings available from users [10]. Algorithms also perform differently depending on the demographic characteristics of users [6][11], depending on Read more…

By Andrew Collins, ago
Recommendations as-a-Service (RaaS)

The Architecture of Mr. DLib’s Scientific Recommender-System API

Our manuscript “The Architecture of Mr. DLib’s Scientific Recommender-System API” got accepted at the “26th Irish Conference on Artificial Intelligence and Cognitive Science” (AICS), and here is the pre-print version (HTML below; PDF on arxiv). The bibliographic BibTeX data is: @InProceedings{Beel2018MDLArch, author = {Beel, Joeran and Collins, Andrew and Aizawa, Akiko}, title = {The Architecture of Mr. DLib’s Scientific Recommender-System API}, booktitle = {Proceedings of the 26th Irish Conference on Artificial Intelligence and Cognitive Science}, year = {2018}, volume = {26}, volumes = {1}, number = {26}, pages = {1–12}, abstract = {Recommender systems in academia are not widely available. This may be in part due to the difficulty and cost of developing and maintaining recommender systems. Many operators of Read more…

By Joeran Beel, ago
Recommender Systems

Report/Photos from the 12th ACM Conference on Recommender Systems in Vancouver

Today, the 12th ACM Conference on Recommender Systems began in Vancouver, Canada. We attend and will present our work on meta-learning reference parsing tools in which we treat reference extraction from scientific articles as a recommendation problem. If you also attend the conference, visit us during the poster session on Thursday. So far, it has been a great conference with many exciting talks. A few of the many great presentations are the following Why I like it: Multi-task Learning for Recommendation and Explanation We describe a novel, multi-task recommendation model, which jointly learns to perform rating prediction and recommendation explanation by combining matrix factorization, for rating prediction, and adversarial sequence to sequence learning for explanation generation. The result is evaluated using real-world Read more…

By Joeran Beel, ago
Recommender Systems

“Research-Paper Recommender Systems: A Literature Survey” now available open access

“Research-Paper Recommender Systems: A Literature Survey”, our survey on recommender systems for research articles and citations is now available open access on Springer via ReadCube https://rdcu.be/5qT7. This survey is our most cited paper (241 citations according to Google Scholar), and we are glad that it is now available for free for anyone.

By Joeran Beel, ago
Machine Learning

ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing (Pre-Print)

We are delighted to announce that our poster “ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing” has been accepted at the 12th ACM Recommender Systems Conference (RecSys) for presentation in Vancouver, Canada. The pre-print is available on arXiv, and here in our blog: Abstract Bibliographic reference parsers extract metadata (e.g. author names, title, year) from bibliographic reference strings. No reference parser consistently gives the best results in every scenario. For instance, one tool may be best in extracting titles, and another tool in extracting author names. In this paper, we address the problem of reference parsing from a recommender-systems perspective. We propose ParsRec, a meta-learning approach that recommends the potentially best parser(s) for a given reference string. We evaluate ParsRec on 105k Read more…

By Joeran Beel, ago
Recommender Systems

Mr. DLib’s Living Lab for Scholarly Recommendations (preprint)

We published a manuscript on arXiv about the first living lab for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for scholarly recommendations, i.e., research papers, citations, conferences, research grants etc. Recommendations are delivered through the living lab´s API in platforms such as reference management software and digital libraries. The living lab is built on top of the recommender system as-a-service Mr. DLib. Current partners are the reference management software JabRef and the CORE research team. We present the architecture of Mr. DLib’s living lab as well as usage statistics on the first ten months of operating it. During this time, 970,517 recommendations were delivered with a mean click-through rate of 0.22%. Read more…

By Joeran Beel, ago
Recommender Systems

RARD II: The 2nd Related-Article Recommendation Dataset (preprint)

We released a new version of RARD, i.e. RARD II and describe the new release in a preprint published on arXiv. The dataset is available at http://data.mr-dlib.org and the new manuscript is available on arXiv and here in our Blog. The main contribution of this paper is to introduce and describe a new recommender-systems dataset (RARD II). It is based on data from a recommender-system in the digital library and reference management software domain. As such, it complements datasets from other domains such as books, movies, and music. The RARD II dataset encompasses 89m recommendations, covering an item-space of 24m unique items. RARD II provides a range of rich recommendation data, beyond conventional ratings. For example, in addition to the usual rating Read more…

By Joeran Beel, ago
Recommender Systems

Our website ranks #1 for ‘recommender systems ireland’ and ‘recommender systems dublin’ searches on Google

We started working at Trinity College Dublin 1.5 years ago and launched our new website only 2 months ago. Yet, Google ranks our website #1 for the search queries ‘recommender systems ireland‘ and ‘recommender systems dublin‘ and, not surprisingly, for the variations ‘ireland recommender systems‘ and ‘dublin recommender systems‘. Of course, this is not to mean that we, the School of Computer Science and Statistics at Trinity College Dublin or the ADAPT Centre are the undisputed authorities in the field of recommender systems in Dublin or Ireland. There are several notable researchers and institutions more including Prof. Barry Smyth, Dr Derek Bridge and the Insight Centre. However, this good Google ranking is a flattering approval of our work in the field of recommender systems. For more details on our work please Read more…

By Joeran Beel, ago
Conferences

New publication: Position Bias in Recommender Systems for Digital Libraries

We recently had a paper accepted to iConference. We used click data from Mr DLib, our recommender-as-a-service, to see if users of recommender systems in digital libraries are affected by position bias. We found that users are affected by position bias on average. Some users do seem to examine recommendations more carefully than other users. As far as we know, this is the only investigation of position bias in digital libraries, and in recommender systems for digital libraries. We have made the data available. It contains 10 million recommendations delivered by us to users, with 12,543 clicks on recommendations logged. We hope that other researchers will find this useful. For example, perhaps the click behavior of a digital library user could Read more…

By Andrew Collins, ago