Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2019

Lex Elisabeth, Kowald Dominik, Schedl Markus

Modeling Popularity and Temporal Drift of Music Genre Preference

Transactions of the International Society for Music Information Retrieval (TISMIR, 2019

Journal
2019

Duricic Tomislav, Lacic Emanuel, Kowald Dominik, Lex Elisabeth

Exploiting weak ties in trust-based recommender systems using regular equivalence

EUROCSS'2019, 2019

Konferenz
User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. CF, however, suffers from data sparsity and the cold-start problem since users often rate only a small fraction of available items. One solution is to incorporate additional information into the recommendation process such as explicit trust scores that are assigned by users to others or implicit trust relationships that result from social connections between users. Such relationships typically form a very sparse trust network, which can be utilized to generate recommendations for users based on people they trust. In our work, we explore the use of regular equivalence applied to a trust network to generate a similarity matrix that is used for selecting k-nearest neighbors used for item recommendation. Two vertices in a network are regularly equivalent if their neighbors are themselves equivalent and by using the iterative approach of calculating regular equivalence, we can study the impact of strong and weak ties on item recommendation. We evaluate our approach on cold start users on a dataset crawled from Epinions and find that by using weak ties in addition to strong ties, we can improve the performance of a trust-based recommender in terms of recommendation accuracy.
2019

Kowald Dominik, Lex Elisabeth, Schdel Markus

Modeling Artist Preferences for Personalized Music Recommendation

In Late-Breaking-Results Track of the 20th annual conference of the International Society for Music Information Retrieval (ISMIR'2019, 2019

Konferenz
2019

Kopeinik Simone, Lex Elisabeth, Kowald Dominik, Albert Dietrich, Seitlinger Paul

A Real-Life School Study of Confirmation Bias and Polarisation in Information Behaviou

Lecture Notes in Computer Science, Springer, 2019

Konferenz
When people engage in Social Networking Sites, they influence one another through their contributions. Prior research suggests that the interplay between individual differences and environmental variables, such as a person’s openness to conflicting information, can give rise to either public spheres or echo chambers. In this work, we aim to unravel critical processes of this interplay in the context of learning. In particular, we observe high school students’ information behavior (search and evaluation of Web resources) to better understand a potential coupling between confirmatory search and polarization and, in further consequence, improve learning analytics and information services for individual and collective search in learning scenarios. In an empirical study, we had 91 high school students performing an information search in a social bookmarking environment. Gathered log data was used to compute indices of confirmatory search and polarisation as well as to analyze the impact of social stimulation. We find confirmatory search and polarization to correlate positively and social stimulation to mitigate, i.e., reduce the two variables’ relationship. From these findings, we derive practical implications for future work that aims to refine our formalism to compute confirmatory search and polarisation indices and to apply it for depolarizing information services
2019

Kowald Dominik, Traub Matthias, Theiler Dieter, Gursch Heimo, Lacic Emanuel, Lindstaedt Stefanie , Kern Roman, Lex Elisabeth

Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets

REVEAL Workshop co-located with RecSys'2019, ACM, 2019

Konferenz
2019

Kowald Dominik, Lacic Emanuel, Theiler Dieter, Traub Matthias, Kuffer Lucky, Lindstaedt Stefanie , Lex Elisabeth

Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metrik

REVEAL Workshop co-located with RecSys'2019, ACM, Kopenhagen, Denmark, 2019

Konferenz
2019

Kowald Dominik, Lex Elisabeth, Schedl Markus

Modeling Artist Preferences of Users with Different Music Consumption Patterns for Fair Music Recommendation

European Symposium on Computational Social Science (EuroCSS), Zurich, Switzerland, 2019

Konferenz
2019

Lex Elisabeth, Kowald Dominik

The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approac

49th GI Annual Conference (INFORMATIK'2019), Kassel, Germany, 2019

Konferenz
2019

Adolfo Ruiz Calleja, Dennerlein Sebastian, Kowald Dominik, Theiler Dieter, Lex Elisabeth, Tobias Ley

An Infrastructure for Workplace Learning Analytics: Tracing Knowledge Creation with the Social Semantic Server

Journal of Learning Analytics, Society for Learning Analytics Research (SoLAR), UTS ePress , 2019

Journal
In this paper, we propose the Social Semantic Server (SSS) as a service-based infrastructure for workplace andprofessional Learning Analytics (LA). The design and development of the SSS has evolved over 8 years, startingwith an analysis of workplace learning inspired by knowledge creation theories and its application in differentcontexts. The SSS collects data from workplace learning tools, integrates it into a common data model based ona semantically-enriched Artifact-Actor Network and offers it back for LA applications to exploit the data. Further,the SSS design promotes its flexibility in order to be adapted to different workplace learning situations. Thispaper contributes by systematizing the derivation of requirements for the SSS according to the knowledge creationtheories, and the support offered across a number of different learning tools and LA applications integrated to it.It also shows evidence for the usefulness of the SSS extracted from four authentic workplace learning situationsinvolving 57 participants. The evaluation results indicate that the SSS satisfactorily supports decision making indiverse workplace learning situations and allow us to reflect on the importance of the knowledge creation theoriesfor such analysis.
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