This data relates to the paper "Position Bias in Recommender Systems for Digital Libraries". During the course of this study, 1.6m sets, each containing 6 recommendations, were delivered to users of a digital library. Of these recommendations delivered, 12,543 clicks were logged. Included here are CSV whose rows describe each of these recommendations. Python scripts are also included to establish the click through rates for this data
Due to growth of World Wide Web, enormous data are created. To get the information out of available ...
Abstract. In academic studies, the evaluation of recommender system (RS) algorithms is often limited...
Peer-reviewedThe present article provides a general review of automatic recommendations tools in dig...
Abstract. Measuring the quality of recommendations produced by a recommender system (RS) is challeng...
This tutorial provides a common ground for both researchers and practitioners interested in data and...
In the world of recommender systems, it is a common practice to use public available datasets from d...
There are inherent problems with evaluating the accuracy of recommender systems. Commonly-used metri...
This repository contains three datasets for evaluating accuracy, miscalibration and popularity lift ...
This data relates to our paper "Stereotype and Most-Popular Recommendations in the Digital Library S...
Decisions are taken by humans very often during professional as well as leisure activities. It is pa...
Traditional collection development relies heavily on human input, with librarians relying on reviews...
Recommender systems in search systems are an established way of pointing the user to related content...
International audienceRecommendation systems have been integrated into the majority of large online ...
Traditional collection development relies heavily on human input, with librarians relying on reviews...
Recommender systems are very important in searching for items all over the internet. There are many ...
Due to growth of World Wide Web, enormous data are created. To get the information out of available ...
Abstract. In academic studies, the evaluation of recommender system (RS) algorithms is often limited...
Peer-reviewedThe present article provides a general review of automatic recommendations tools in dig...
Abstract. Measuring the quality of recommendations produced by a recommender system (RS) is challeng...
This tutorial provides a common ground for both researchers and practitioners interested in data and...
In the world of recommender systems, it is a common practice to use public available datasets from d...
There are inherent problems with evaluating the accuracy of recommender systems. Commonly-used metri...
This repository contains three datasets for evaluating accuracy, miscalibration and popularity lift ...
This data relates to our paper "Stereotype and Most-Popular Recommendations in the Digital Library S...
Decisions are taken by humans very often during professional as well as leisure activities. It is pa...
Traditional collection development relies heavily on human input, with librarians relying on reviews...
Recommender systems in search systems are an established way of pointing the user to related content...
International audienceRecommendation systems have been integrated into the majority of large online ...
Traditional collection development relies heavily on human input, with librarians relying on reviews...
Recommender systems are very important in searching for items all over the internet. There are many ...
Due to growth of World Wide Web, enormous data are created. To get the information out of available ...
Abstract. In academic studies, the evaluation of recommender system (RS) algorithms is often limited...
Peer-reviewedThe present article provides a general review of automatic recommendations tools in dig...