Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


Kowald Dominik, Pujari Suhbash Chandra, Lex Elisabeth

Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach

Proceedings of the 26th International World Wide Web Conference, WWW'2017, ACM, Perth, Western Australia, 2017

Hashtags have become a powerful tool in social platformssuch as Twitter to categorize and search for content, and tospread short messages across members of the social network.In this paper, we study temporal hashtag usage practices inTwitter with the aim of designing a cognitive-inspired hashtagrecommendation algorithm we call BLLI,S. Our mainidea is to incorporate the effect of time on (i) individualhashtag reuse (i.e., reusing own hashtags), and (ii) socialhashtag reuse (i.e., reusing hashtags, which has been previouslyused by a followee) into a predictive model. For this,we turn to the Base-Level Learning (BLL) equation from thecognitive architecture ACT-R, which accounts for the timedependentdecay of item exposure in human memory. Wevalidate BLLI,S using two crawled Twitter datasets in twoevaluation scenarios. Firstly, only temporal usage patternsof past hashtag assignments are utilized and secondly, thesepatterns are combined with a content-based analysis of thecurrent tweet. In both evaluation scenarios, we find not onlythat temporal effects play an important role for both individualand social hashtag reuse but also that our BLLI,S approachprovides significantly better prediction accuracy andranking results than current state-of-the-art hashtag recommendationmethods.

Kowald Dominik, Kopeinik Simone , Lex Elisabeth

The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems

International Conference on User Modeling, Adaptation and Personalization 2017, UMAP'2017, ACM, Bratislava, 2017

Recommender systems have become important tools to supportusers in identifying relevant content in an overloaded informationspace. To ease the development of recommender systems, a numberof recommender frameworks have been proposed that serve a widerange of application domains. Our TagRec framework is one of thefew examples of an open-source framework tailored towards developingand evaluating tag-based recommender systems. In this paper,we present the current, updated state of TagRec, and we summarizeand reƒect on four use cases that have been implemented withTagRec: (i) tag recommendations, (ii) resource recommendations,(iii) recommendation evaluation, and (iv) hashtag recommendations.To date, TagRec served the development and/or evaluation processof tag-based recommender systems in two large scale Europeanresearch projects, which have been described in 17 research papers.‘us, we believe that this work is of interest for both researchersand practitioners of tag-based recommender systems.

Lacic Emanuel, Kowald Dominik, Lex Elisabeth

Tailoring Recommendations for a Multi-Domain Environment

ACM International Conference on Recommender Systems 2017, RecSys'2017, ACM, Como, Italy, 2017

Recommender systems are acknowledged as an essential instrumentto support users in finding relevant information. However,the adaptation of recommender systems to multiple domain-specificrequirements and data models still remains an open challenge. Inthe present paper, we contribute to this sparse line of research withguidance on how to design a customizable recommender systemthat accounts for multiple domains with heterogeneous data. Usingconcrete showcase examples, we demonstrate how to setup amulti-domain system on the item and system level, and we reportevaluation results for the domains of (i) LastFM, (ii) FourSquare,and (iii) MovieLens. We believe that our findings and guidelinescan support developers and researchers of recommender systemsto easily adapt and deploy a recommender system in distributedenvironments, as well as to develop and evaluate algorithms suitedfor multi-domain settings

Kowald Dominik, Lex Elisabeth

Overcoming the Imbalance Between Tag Recommendation Approaches and Real-World Folksonomy Structures with Cognitive-Inspired Algorithm

European Symposium on Computational Social Sciences, ESCSS'2017, ACM, London, 2017

In this paper, we study the imbalance between current state-of-the-art tag recommendation algorithms and the folksonomy structures of real-world social tagging systems. While algorithms such as FolkRank are designed for dense folksonomy structures, most social tagging systems exhibit a sparse nature. To overcome this imbalance, we show that cognitive-inspired algorithms, which model the tag vocabulary of a user in a cognitive-plausible way, can be helpful. Our present approach does this via implementing the activation equation of the cognitive architecture ACT-R, which determines the usefulness of units in human memory (e.g., tags). In this sense, our long-term research goal is to design hybrid recommendation approaches, which combine the advantages of both worlds in order to adapt to the current setting (i.e., sparse vs. dense ones)

d'Aquin Mathieu , Adamou Alessandro , Dietze Stefan , Fetahu Besnik , Gadiraju Ujwal , Hasani-Mavriqi Ilire, Holz Peter, Kümmerle Joachim, Kowald Dominik, Lex Elisabeth, Lopez Sola Susana, Mataran Ricardo, Sabol Vedran, Troullinou Pinelopi, Veas Eduardo, Veas Eduardo Enrique

AFEL: Towards Measuring Online Activities Contributions to Self-Directed Learning

7th Workshop on Awareness and Reflection in Technology Enhanced Learning (ARTEL 2017), Kravcik M., Mikroyannidis A., Pammer-Schindler V., Prilla M., CEUR-WS, Tallinn, Estonia, 2017

More and more learning activities take place online in a self-directed manner. Therefore, just as the idea of self-tracking activities for fitness purposes has gained momentum in the past few years, tools and methods for awareness and self-reflection on one's own online learning behavior appear as an emerging need for both formal and informal learners. Addressing this need is one of the key objectives of the AFEL (Analytics for Everyday Learning) project. In this paper, we discuss the different aspects of what needs to be put in place in order to enable awareness and self-reflection in online learning. We start by describing a scenario that guides the work done. We then investigate the theoretical, technical and support aspects that are required to enable this scenario, as well as the current state of the research in each aspect within the AFEL project. We conclude with a discussion of the ongoing plans from the project to develop learner-facing tools that enable awareness and self-reflection for online, self-directed learners. We also elucidate the need to establish further research programs on facets of self-tracking for learning that are necessarily going to emerge in the near future, especially regarding privacy and ethics.
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