Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

The Social Semantic Server: A Flexible Framework to Support Informal Learning at the Workplace

S Dennerlein, D Kowald, E Lex, D Theiler, E Lacic, T Ley, V Tomberg, A Ruiz-Calleja – …, 2015 – In Proceeding of 15th International Conference …

Informal learning at the workplace includes a multitude of processes. Respective activities can be categorized into multiple perspectives on informal learning, such as reflection, sensemaking, help seeking and maturing of collective knowledge. Each perspective raises requirements with respect to the technical support, this is why an integrated solution relying on…

Smart Booking Without Looking: Providing Hotel Recommendations in the TripRebel Portal

M Traub, D Kowald, E Lacic, P Schoen, G Supp, E Lex – In Proceeding of 15th International Conference on Knowledge Techno…, 2015

In this paper, we present a scalable hotel recommender system for TripRebel, a new online booking portal. On the basis of the opensource enterprise search platform Apache Solr, we developed a system architecture with Web-based services to interact with indexed data at large scale as well as to provide hotel…

Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study

D Kowald, E Lex – ACM, 2015 – 9th ACM Conference on Recommender Systems

To date, the evaluation of tag recommender algorithms has mostly been conducted in limited ways, including p-core pruned datasets, a small set of compared algorithms and solely based on recommender accuracy. In this study, we use an open-source evaluation framework to compare a rich set of state-of-the-art algorithms in six…

Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems

E Lacic, D Kowald, M Traub, G Luzhnica, J Simon, E Lex – ACM, 2015 – 9th ACM Conference on Recommender Systems

In this paper, we present work-in-progress on a recommender system based on Collaborative Filtering that exploits location information gathered by indoor positioning systems. This approach allows us to provide recommendations for "extreme" cold-start users with absolutely no item interaction data available, where methods based on Matrix Factorization would not work.…

ScaR: Towards a Real-Time Recommender Framework Following the Microservices Architecture

E Lacic, D Kowald, M Traub, E Lex – ACM, 2015 – 9th ACM Conference on Recommender Systems

Various recommender frameworks have been proposed, but still there is a lack of work that addresses important aspects like: immediately considering streaming data within the recommendation process; scalability of the recommender system; real-time recommendation based on different context dependent data. To bridge these gaps, we contribute with a novel recommender…

Modeling Cognitive Processes in Social Tagging to Improve Tag Recommendations

D Kowald – International World Wide Web Conference Committee (IW3C2), 2015

With the emergence of Web 2.0, tag recommenders have become important tools, which aim to support users in nding descriptive tags for their bookmarked resources. Although current algorithms provide good results in terms of tag prediction accuracy, they are often designed in a data-driven way and thus, lack a thorough…

Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

P Seitlinger, D Kowald, S Kopeinik, I Hasani-Mavriqi, T Ley, E Lex – In 24rd International World Wide Web Conference (WW…, 2015

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

D Kowald, S Kopeinik, P Seitlinger, T Ley, D Albert, C Trattner – MSM-MUSE PostProceedings, 2015

In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory. Based on a theory of human memory, the approach estimates a tag's probability being applied by a particular user as a function of usage frequency and recency of the…

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

D Kowald, P Seitlinger, S Kopeinik, T Ley, C Trattner – MSM-MUSE PostProceedings, 2015

We assume that recommender systems are more successful, when they are based on a thorough understanding of how people process information. In the current paper we test this assumption in the context of social tagging systems. Cognitive research on how people assign tags has shown that they draw on two…

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

E Lacic, D Kowald, L Eberhard, C Trattner, D Parra, L Marinho – MSM-MUSE PostProceedings, 2015

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…

TagRec: Towards A Standardized Tag Recommender Benchmarking Framework

D Kowald, E Lacic, C Trattner – In Proceedings of the 25th ACM Conference on Hypertext and Social Media (HT 2014), 2014

In this paper, we introduce TagRec, a standardized tag recommender benchmarking framework implemented in Java. The purpose of TagRec is to provide researchers with a framework that supports all steps of the development process of a new tag recommendation algorithm in a reproducible way, including methods for data pre-processing, data…

SocRecM: A Scalable Social Recommender Engine for Online Marketplaces

E Lacic, D Kowald, C Trattner – In Proceedings of the 25th ACM Conference on Hypertext and Social Media (HT 2014), 2014

In this paper, we present work-in-progress on SocRecM, a novel social recommendation framework for online marketplaces. We demonstrate that SocRecM is not only easy to integrate with existing Web technologies through a RESTful, scalable and easy-to-extend service-based architecture but also reveal the extent to which various social features and recommendation…

Recommending Items in Social Tagging Systems Using Tag and Time Information

E Lacic, D Kowald, P Seitlinger, C Trattner, D Parra – In Proceedings of the 1st Social Personalization Workshop co-loca…, 2014

In this work we present a novel item recommendation approach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and…

Long Time No See: The Probability of Reusing Tags as a Function of Frequency and Recency

D Kowald, P Seitlinger, C Trattner, T Ley – Proceedings of the…, 2014 – International World Wide Web Conferences Steering …

In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term mem- ory. This approach uses the frequency and recency of previous tag assignments to estimate the probability of reusing a particular tag. Using three real-world folksonomies gathered from bookmarks in…

Towards a Scalable Social Recommender Engine for Online Marketplaces: The Case of Apache Solr

E Lacic, D Kowald, D Parra, M Kahr, C Trattner – Proceedings o…, 2014 – International World Wide Web Conferences Steering …

Recent research has unveiled the importance of online social networks for improving the quality of recommenders in several domains, what has encouraged the research community to investigate ways to better exploit the social information for recommendations. However, there is a lack of work that offers details of frameworks that allow…

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