In this study, we explore the citedness of research data, its distribution over time and its relation to the availability of a digital object identifier (DOI) in the Thomson Reuters database Data Citation Index (DCI). We investigate if cited research data “impacts” the (social) web, reflected by altmetrics scores, and if there is any relationship between the number of citations and the sum of altmetrics scores from various social media platforms. Three tools are used to collect altmetrics scores, namely PlumX, ImpactStory, and Altmetric.com, and the corresponding results are compared. We found that out of the three altmetrics tools, PlumX has the best coverage. Our experiments revealed that research data remain mostly uncited (about 85 %), although there has been an increase in citing data sets published since 2008. The percentage of the number of cited research data with a DOI in DCI has decreased in the last years. Only nine repositories are responsible for research data with DOIs and two or more citations. The number of cited research data with altmetrics “foot-prints” is even lower (4–9 %) but shows a higher coverage of research data from the last decade. In our study, we also found no correlation between the number of citations and the total number of altmetrics scores. Yet, certain data types (i.e. survey, aggregate data, and sequence data) are more often cited and also receive higher altmetrics scores. Additionally, we performed citation and altmetric analyses of all research data published between 2011 and 2013 in four different disciplines covered by the DCI. In general, these results correspond very well with the ones obtained for research data cited at least twice and also show low numbers in citations and in altmetrics. Finally, we observed that there are disciplinary differences in the availability and extent of altmetrics scores.

Research has shown that recommender systems are typically biased towards popular items, which leads to less popular items being underrepresented in recommendations. The recent work of Abdollahpouri et al. in the context of movie recommendations has shown that this popularity bias leads to unfair treatment of both long-tail items as well as users with little interest in popular items. In this paper, we reproduce the analyses of Abdollahpouri et al. in the context of music recommendation. Specifically, we investigate three user groups from the Last.fm music platform that are categorized based on how much their listening preferences deviate from the most popular music among all Last.fm users in the dataset: (i) low-mainstream users, (ii) medium-mainstream users, and (iii) high-mainstream users. In line with Abdollahpouri et al., we find that state-of-the-art recommendation algorithms favor popular items also in the music domain. However, their proposed Group Average Popularity metric yields different results for Last.fm than for the movie domain, presumably due to the larger number of available items (i.e., music artists) in the Last.fm dataset we use. Finally, we compare the accuracy results of the recommendation algorithms for the three user groups and find that the low-mainstreaminess group significantly receives the worst recommendations.

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In this paper, we study factors that influence tag reuse behavior in social tagging systems. Our work is guided by the activation equation of the cognitive model ACT-R, which states that the usefulness of information in human memory depends on the three factors usage frequency, recency and semantic context. It is our aim to shed light on the influence of these factors on tag reuse. In our experiments, we utilize six datasets from the social tagging systems Flickr, CiteULike, BibSonomy, Delicious, LastFM and MovieLens, covering a range of various tagging settings. Our results confirm that frequency, recency and semantic context positively influence the reuse probability of tags. However, the extent to which each factor individually influences tag reuse strongly depends on the type of folksonomy present in a social tagging system. Our work can serve as guideline for researchers and developers of tag-based recommender systems when designing algorithms for social tagging environments.

To date, the evaluation of tag recommender algorithms has mostly been conducted in limited ways, including p-core pruned datasets, a small set of compared algorithms and solely based on recommender accuracy. In this study, we use an open-source evaluation framework to compare a rich set of state-of-the-art algorithms in six unfiltered, open datasets via various metrics, measuring not only accuracy but also the diversity, novelty and computational costs of the approaches. We therefore provide a transparent and reproducible tag recommender evaluation in real-world folksonomies. Our results suggest that the efficacy of an algorithm highly depends on the given needs and thus, they should be of interest to both researchers and developers in the field of tag-based recommender systems.