Lacic Emanuel, Reiter-Haas Markus, Duricic Tomislav, Slawicek Valentin, Lex Elisabeth
2019
In this work, we present the findings of an online study, where we explore the impact of utilizing embeddings to recommend job postings under real-time constraints. On the Austrian job platform Studo Jobs, we evaluate two popular recommendation scenarios: (i) providing similar jobs and, (ii) personalizing the job postings that are shown on the homepage. Our results show that for recommending similar jobs, we achieve the best online performance in terms of Click-Through Rate when we employ embeddings based on the most recent interaction. To personalize the job postings shown on a user's homepage, however, combining embeddings based on the frequency and recency with which a user interacts with job postings results in the best online performance.
Duricic Tomislav, Lacic Emanuel, Kowald Dominik, Lex Elisabeth
2019
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.
Kowald Dominik, Lex Elisabeth, Schdel Markus
2019
Kopeinik Simone, Seitlinger Paul, Lex Elisabeth
2019
Kopeinik Simone, Lex Elisabeth, Kowald Dominik, Albert Dietrich, Seitlinger Paul
2019
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
Kowald Dominik, Traub Matthias, Theiler Dieter, Gursch Heimo, Lacic Emanuel, Lindstaedt Stefanie , Kern Roman, Lex Elisabeth
2019
Kowald Dominik, Lacic Emanuel, Theiler Dieter, Traub Matthias, Kuffer Lucky, Lindstaedt Stefanie , Lex Elisabeth
2019
Kowald Dominik, Lex Elisabeth, Schedl Markus
2019
Lex Elisabeth, Kowald Dominik
2019
Adolfo Ruiz Calleja, Dennerlein Sebastian, Kowald Dominik, Theiler Dieter, Lex Elisabeth, Tobias Ley
2019
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.