Lex Elisabeth, Kowald Dominik, Seitlinger Paul, Tran Tran, Felfernig Alexander, Schedl Markus
2021
Psychology-informed Recommender Systems
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, Seitlinger Paul , Ley Tobias , Lex Elisabeth
2018
In this paper, we present the results of an online study with the aim to shed light on the impact that semantic context cues have on the user acceptance of tag recommendations. Therefore, we conducted a work-integrated social bookmarking scenario with 17 university employees in order to compare the user acceptance of a context-aware tag recommendation algorithm called 3Layers with the user acceptance of a simple popularity-based baseline. In this scenario, we validated and verified the hypothesis that semantic context cues have a higher impact on the user acceptance of tag recommendations in a collaborative tagging setting than in an individual tagging setting. With this paper, we contribute to the sparse line of research presenting online recommendation studies.
Seitlinger Paul, Ley Tobias, Kowald Dominik, Theiler Dieter, Hasani-Mavriqi Ilire, Dennerlein Sebastian, Lex Elisabeth, Albert D.
2017
Creative group work can be supported by collaborative search and annotation of Web resources. In this setting, it is important to help individuals both stay fluent in generating ideas of what to search next (i.e., maintain ideational fluency) and stay consistent in annotating resources (i.e., maintain organization). Based on a model of human memory, we hypothesize that sharing search results with other users, such as through bookmarks and social tags, prompts search processes in memory, which increase ideational fluency, but decrease the consistency of annotations, e.g., the reuse of tags for topically similar resources. To balance this tradeoff, we suggest the tag recommender SoMe, which is designed to simulate search of memory from user-specific tag-topic associations. An experimental field study (N = 18) in a workplace context finds evidence of the expected tradeoff and an advantage of SoMe over a conventional recommender in the collaborative setting. We conclude that sharing search results supports group creativity by increasing the ideational fluency, and that SoMe helps balancing the evidenced fluency-consistency tradeoff.
Kopeinik Simone, Lex Elisabeth, Seitlinger Paul, Ley Tobias, Albert Dietrich
2017
In online social learning environments, tagging has demonstratedits potential to facilitate search, to improve recommendationsand to foster reflection and learning.Studieshave shown that shared understanding needs to be establishedin the group as a prerequisite for learning. We hypothesisethat this can be fostered through tag recommendationstrategies that contribute to semantic stabilization.In this study, we investigate the application of two tag recommendersthat are inspired by models of human memory:(i) the base-level learning equation BLL and (ii) Minerva.BLL models the frequency and recency of tag use while Minervais based on frequency of tag use and semantic context.We test the impact of both tag recommenders on semanticstabilization in an online study with 56 students completinga group-based inquiry learning project in school. Wefind that displaying tags from other group members contributessignificantly to semantic stabilization in the group,as compared to a strategy where tags from the students’individual vocabularies are used. Testing for the accuracyof the different recommenders revealed that algorithms usingfrequency counts such as BLL performed better whenindividual tags were recommended. When group tags wererecommended, the Minerva algorithm performed better. Weconclude that tag recommenders, exposing learners to eachother’s tag choices by simulating search processes on learners’semantic memory structures, show potential to supportsemantic stabilization and thus, inquiry-based learning ingroups.
Trattner Christoph, Kowald Dominik, Seitlinger Paul, Ley Tobias
2016
Several successful tag recommendation mechanisms have been developed, including algorithms built upon Collaborative Filtering, Tensor Factorization, graph-based and simple "most popular tags" approaches. From an economic perspective, the latter approach has been convincing since calculating frequencies is computationally efficient and effective with respect to different recommender evaluation metrics. In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory in order to extend these conventional "most popular tags" approaches. Based on a theory of human memory, the approach estimates a tag's reuse probability as a function of usage frequency and recency in the user's past (base-level activation) as well as of the current semantic context (associative component).Using four real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike, Delicious and Flickr, we show how refining frequency-based estimates by considering recency and semantic context outperforms conventional "most popular tags" approaches and another existing and very effective but less theory-driven, time-dependent recommendation mechanism. By combining our approach with a simple resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as Collaborative Filtering, FolkRank and Pairwise Interaction Tensor Factorization with respect to recommender accuracy and runtime. We conclude that our approach provides an accurate and computationally efficient model of a user's temporal tagging behavior. Moreover, we demonstrate how effective principles of recommender systems can be designed and implemented if human memory processes are taken into account.
Dennerlein Sebastian, Ley Tobias, , Lex Elisabeth, Seitlinger Paul
2016
In the digital realm, meaning making is reflected in the reciprocal manipulation of mediating artefacts. We understand uptake, i.e. interaction with and understanding of others’ artefact interpretations, as central mechanism and investigate its impact on individual and social learning at work. Results of our social tagging field study indicate that increased uptake of others’ tags is related to a higher shared understanding of collaborators as well as narrower and more elaborative exploration in individual information search. We attribute the social and individual impact to accommodative processes in the high uptake condition.
Seitlinger Paul, Kowald Dominik, Kopeinik Simone, Hasani-Mavriqi Ilire, Ley Tobias, Lex Elisabeth
2015
Classic resource recommenders like Collaborative Filtering(CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and inter-pretation. In this paper, we propose a novel hybrid rec-ommendation strategy that re nes CF by capturing thesedynamics. The evaluation results reveal that our approachsubstantially improves CF and, depending on the dataset,successfully competes with a computationally much moreexpensive Matrix Factorization variant.
Kowald Dominik, Seitlinger Paul, Kopeinik Simone, Ley Tobias, Trattner Christoph
2015
We assume that recommender systems are more successful,when they are based on a thorough understanding of how people processinformation. In the current paper we test this assumption in the contextof social tagging systems. Cognitive research on how people assign tagshas shown that they draw on two interconnected levels of knowledge intheir memory: on a conceptual level of semantic fields or LDA topics,and on a lexical level that turns patterns on the semantic level intowords. Another strand of tagging research reveals a strong impact oftime-dependent forgetting on users' tag choices, such that recently usedtags have a higher probability being reused than "older" tags. In thispaper, we align both strands by implementing a computational theory ofhuman memory that integrates the two-level conception and the processof forgetting in form of a tag recommender. Furthermore, we test theapproach in three large-scale social tagging datasets that are drawn fromBibSonomy, CiteULike and Flickr.As expected, our results reveal a selective effect of time: forgetting ismuch more pronounced on the lexical level of tags. Second, an extensiveevaluation based on this observation shows that a tag recommender interconnectingthe semantic and lexical level based on a theory of humancategorization and integrating time-dependent forgetting on the lexicallevel results in high accuracy predictions and outperforms other wellestablishedalgorithms, such as Collaborative Filtering, Pairwise InteractionTensor Factorization, FolkRank and two alternative time-dependentapproaches. We conclude that tag recommenders will benefit from goingbeyond the manifest level of word co-occurrences, and from includingforgetting processes on the lexical level.
Kowald Dominik, Kopeinik S., Seitlinger Paul, Trattner Christoph, Ley Tobias
2015
In this paper, we introduce a tag recommendation algorithmthat mimics the way humans draw on items in their long-term memory.Based on a theory of human memory, the approach estimates a tag'sprobability being applied by a particular user as a function of usagefrequency and recency of the tag in the user's past. This probability isfurther refined by considering the inuence of the current semantic contextof the user's tagging situation. Using three real-world folksonomiesgathered from bookmarks in BibSonomy, CiteULike and Flickr, we showhow refining frequency-based estimates by considering usage recency andcontextual inuence outperforms conventional "most popular tags" approachesand another existing and very effective but less theory-driven,time-dependent recommendation mechanism.By combining our approach with a simple resource-specific frequencyanalysis, our algorithm outperforms other well-established algorithms,such as FolkRank, Pairwise Interaction Tensor Factorization and CollaborativeFiltering. We conclude that our approach provides an accurateand computationally efficient model of a user's temporal tagging behavior.We demonstrate how effective principles of recommender systemscan be designed and implemented if human memory processes are takeninto account.
Ley Tobias, Seitlinger Paul
2010
Researching the emergence of semantics in social systems needs totake into account how users process information in their cognitive system. Wereport results of an experimental study in which we examined the interactionbetween individual expertise and the basic level advantage in collaborative tagging.The basic level advantage describes availability in memory of certain preferredlevels of taxonomic abstraction when categorizing objects and has beenshown to vary with level of expertise. In the study, groups of students taggedinternet resources for a 10-week period. We measured the availability of tags inmemory with an association test and a relevance rating and found a basic leveladvantage for tags from more general as opposed to specific levels of the taxonomy.An interaction with expertise also emerged. Contrary to our expectations,groups that spent less time to develop a shared understanding shifted tomore specific levels as compared to groups that spent more time on a topic. Weattribute this to impaired collaboration in the groups. We discuss implicationsfor personalized tag and resource recommendations.
Ley Tobias, Lindstaedt Stefanie , Schöfegger Karin, Seitlinger Paul, Weber Nicolas, Hu Bo, Riss Uwe, Brun Roman, Hinkelmann Knut, Thönssen Barbara, Maier Ronald, Schmidt Andreas
2009