Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2015

Kowald Dominik, Kopeinik S., Seitlinger Paul, Trattner Christoph, Ley Tobias

Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context

Mining, Modeling, and Recommending'Things' in Social Media, MSM'2015, Springer, 2015

Buch
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.
2015

Kowald Dominik, Seitlinger Paul, Kopeinik Simone, Ley Tobias, Trattner Christoph

Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender

Mining, Modeling, and Recommending'Things' in Social Media, MSM'2015, Springer, 2015

Buch
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.
2015

Lacic Emanuel, Kowald Dominik, Eberhard Lukas, Trattner Christoph, Parra Denis, Marinho Leandro

Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces

Mining, Modeling, and Recommending'Things' in Social Media, MSM'2015, Springer, 2015

Buch
Recent research has unveiled the importance of online social networks for improving the quality of recommender systems and encouraged the research community to investigate better ways of exploiting the social information for recommendations. To contribute to this sparse field of research, in this paper we exploit users’ interactions along three data sources (marketplace, social network and location-based) to assess their performance in a barely studied domain: recommending products and domains of interests (i.e., product categories) to people in an online marketplace environment. To that end we defined sets of content- and network-based user similarity features for each data source and studied them isolated using an user-based Collaborative Filtering (CF) approach and in combination via a hybrid recommender algorithm, to assess which one provides the best recommendation performance. Interestingly, in our experiments conducted on a rich dataset collected from SecondLife, a popular online virtual world, we found that recommenders relying on user similarity features obtained from the social network data clearly yielded the best results in terms of accuracy in case of predicting products, whereas the features obtained from the marketplace and location-based data sources also obtained very good results in case of predicting categories. This finding indicates that all three types of data sources are important and should be taken into account depending on the level of specialization of the recommendation task.
2015

Silva Nelson, Eggeling Eva, Schreck Tobias, Fellner Dieter W.

Increasing Fault Tolerance in Operational Centres Using Human Sensing Technologies: Approach and Initial Results

European Project Space on Computer Vision, 2015

Buch
2015

Kravcik Milos, Mikroyannidis Alexander, Pammer-Schindler Viktoria, Prilla Michael , Ullmann T.D.

Proceedings of the 5th Workshop on Awareness and Reflection in Technology Enhanced Learning.  In conjunction with the 10th European Conference on Technology Enhanced Learning: Design for Teaching and Learning in a Networked World

ARTEL 2015 Awareness and Reflection in Technology Enhanced Learning , 2015

Buch
2015

Lindstaedt Stefanie , Ley Tobias, Sack Harald

Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business

i-KNOW '15 15th International Conference on Knowledge Technologies and Data-Driven Business, 2015

Buch
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