activity
20132023
most citedA Survey on Popularity Bias in Recommender Systems

133 citations · 249 across the 13 of their papers we have counts for

collaborators
Showing 2014Show all

5 papers · 1 filter

cs.IR2014★ 8 cited

Recommending Items in Social Tagging Systems Using Tag and Time Information

Emanuel Lacic, Dominik Kowald, Paul Seitlinger +2

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…

cs.IR2014★ 1 cited

SocRecM: A Scalable Social Recommender Engine for Online Marketplaces

Emanuel Lacic, Dominik Kowald, Christoph Trattner

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 wi…

cs.IR2014★ 11 cited

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

Emanuel Lacic, Dominik Kowald, Lukas Eberhard +3

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 w…

cs.SI2014★ 4 cited

Twitter in Academic Conferences: Usage, Networking and Participation over Time

Xidao Wen, Yu-Ru Lin, Christoph Trattner +1

Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, attendees and virtual attendees socialize, introduce new…

cs.IR2014★ 1 cited

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

Dominik Kowald, Paul Seitlinger, Christoph Trattner +1

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 assumpt…