Green Recommender Systems: Down-Sampling Datasets for Energy-Efficient Algorithm Performance

Abstract As recommender systems become increasingly prevalent, the environmental impact and energy efficiency of training these large-scale models have come under scrutiny. This paper investigates the potential for energy-efficient algorithm performance by optimizing dataset sizes through downsampling techniques. We conducted experiments on the MovieLens 100K, 1M, 10M and Amazon Toys Read more…

8 Recommender Systems Illustration

ISG will present 8 papers and posters at the ACM Recommender Systems Conference and Workshops

We are thrilled that 8 of our 11 submissions to the 18th ACM Recommender-Systems Conference and Workshops (RobustRecSys and RecSoGood) were accepted for publication. Our research was conducted jointly with partners from the University of Gothenburg (Alan Said), the University of Antwerpen (Lien Michiels), and some excellent Bachelor and Master Read more…

e fold and not k fold (Green Recommender Systems)

From Theory to Practice: Implementing and Evaluating e-Fold Cross-Validation

Accepted for publication at the International Conference on Artificial Intelligence and Machine Learning Research (CAIMLR). The PDF is available here. Feel free to also read the original proposal that led to the current publication. Abstract In this paper, we present e-fold cross-validation, an energy-efficient alternative to k-fold, which dynamically adjusts Read more…

From Clicks to Carbon: The Ecological Costs of Recommender Systems (Pre-Print)

Full pre-print as PDF: https://arxiv.org/abs/2408.08203 Abstract As global warming soars, the need to assess the environmental impact of research is becoming increasingly urgent. Despite this, few recommender systems research papers address their environmental impact. In this study, we estimate the ecological impact of recommender systems research by reproducing typical experimental Read more…

Synthetic vs. Real Reference Strings for Citation Parsing, and the Importance of Re-training and Out-Of-Sample Data for Meaningful Evaluations: Experiments with GROBID, GIANT and CORA [pre-print]

ABSTRACT Citation parsing, particularly with deep neural networks, suffers from a lack of training data as available datasets typically contain only a few thousand training instances. Manually labelling citation strings is very time-consuming, hence synthetically created training data could be a solution. However, as of now, it is unknown if Read more…

Keyphrase counts and their effect on clickthrough rates (CTR)

Document Embeddings vs. Keyphrases vs. Terms: An Online Evaluation in Digital Library Recommender Systems

Our paper “Document Embeddings vs. Keyphrases vs. Terms: An Online Evaluation in Digital Library Recommender Systems” was accepted for publication at the ACM/IEEE Joint Conference on Digital Libraries. 1 Introduction Many recommendation algorithms are available to operators of recommender systems in digital libraries. The effectiveness of algorithms in real-world systems is Read more…

RARD I: The Related-Article Recommender-System Dataset

RARD: The Related-Article Recommendation Dataset

We are proud to announce the release of ‘RARD’, the related-article recommendation dataset from the digital library Sowiport and the recommendation-as-a-service provider Mr. DLib. The dataset contains information about 57.4 million recommendations that were displayed to the users of Sowiport. Information includes details on which recommendation approaches were used (e.g. content-based Read more…

Several new publications: Mr. DLib, Lessons Learned, Choice Overload, Bibliometrics (Mendeley Readership Statistics), Apache Lucene, CC-IDF, TF-IDuF

In the past few weeks, we published (or received acceptance notices for) a number of papers related to Mr. DLib, research-paper recommender systems, and recommendations-as-a-service. Many of them were written during our time at the NII or in collaboration with the NII. Here is the list of publications: Beel, Joeran, Bela Gipp, Read more…

Paper accepted at ISI conference in Berlin: “Stereotype and Most-Popular Recommendations in the Digital Library Sowiport”

Our paper titled “Stereotype and Most-Popular Recommendations in the Digital Library Sowiport” is accepted for publication at the 15th International Symposium on Information Science (ISI) in Berlin. Abstract: Stereotype and most-popular recommendations are widely neglected in the research-paper recommender-system and digital-library community. In other domains such as movie recommendations and hotel Read more…

New Paper: On the Robustness of Google Scholar against Spam

I am currently in Toronto presenting our new paper titled “On the Robustness of Google Scholar against Spam” at Hypertext 2010. The paper is about some experiments we did on Google Scholar to find out how reliable their citation data etc. is. The paper soon will be downloadable on our publication page but for now i will post a pre-print version of that paper here in the blog:

Abstract

In this research-in-progress paper we present the current results of several experiments in which we analyzed whether spamming Google Scholar is possible. Our results show, it is possible: We ‘improved’ the ranking of articles by manipulating their citation counts and we made articles appear in searchers for keywords the articles did not originally contained by placing invisible text in modified versions of the article.

1.    Introduction

Researchers should have an interest in having their articles indexed by Google Scholar and other academic search engines such as CiteSeer(X). The inclusion of their articles in the index improves the ability to make their articles available to the academic community. In addition, authors should not only be concerned about the fact that their articles are indexed, but also where they are displayed in the result list. As with all ranked search results, articles displayed in top positions are more likely to be read.

In recent studies we researched the ranking algorithm of Google Scholar [/fusion_builder_column][fusion_builder_column type=”1_1″ background_position=”left top” background_color=”” border_size=”” border_color=”” border_style=”solid” spacing=”yes” background_image=”” background_repeat=”no-repeat” padding=”” margin_top=”0px” margin_bottom=”0px” class=”” id=”” animation_type=”” animation_speed=”0.3″ animation_direction=”left” hide_on_mobile=”no” center_content=”no” min_height=”none”][1-3] and gave advice to researchers on how to optimize their scholarly literature for Google Scholar [4]. However, there are provisos in the academic community against what we called “Academic Search Engine Optimization” [4]. There is the concern that some researchers might use the knowledge about ranking algorithms to ‘over optimize’ their papers in order to push their articles’ rankings in non-legitimate ways.

We conducted some experiments to find out how robust Google Scholar is against spamming. The experiments are not all completed yet but those that are completed show interesting results which are presented in this paper. (more…)

Academic Search Engine Optimization: What others think about it

In January we published our article about Academic Search Engine Optimization (ASEO). As expected, feedback varied strongly. Here are some of the opinions on ASEO:

Search engine optimization (SEO) has a golden age in this internet era, but to use it in academic research, it sounds quite strange for me. After reading this publication (pdf) focusing on this issue, my opinion changed.

[/fusion_builder_column][fusion_builder_column type=”1_1″ background_position=”left top” background_color=”” border_size=”” border_color=”” border_style=”solid” spacing=”yes” background_image=”” background_repeat=”no-repeat” padding=”” margin_top=”0px” margin_bottom=”0px” class=”” id=”” animation_type=”” animation_speed=”0.3″ animation_direction=”left” hide_on_mobile=”no” center_content=”no” min_height=”none”][…] on first impressions it sounds like the stupidest idea I’ve ever heard.

ASEO sounds good to me. I think it’s a good idea.

Good Article..

As you have probably guessed from the above criticisms, I thought that the article was a piece of crap.

In my opinion, being interested in how (academic) search engines function and how scientific papers are indexed and, of course, responding to these… well… circumstances of the scientific citing business is just natural.

Check out the following Blogs to read more about it (some in German and Dutch) (more…)