68 citations · 145 across the 20 of their papers we have counts for
7 papers · 1 filter
Mitigating Confirmation Bias on Twitter by Recommending Opposing Views
Elisabeth Lex, Mario Wagner, Dominik Kowald
In this work, we propose a content-based recommendation approach to increase exposure to opposing beliefs and opinions. Our aim is to help provide users with more diverse viewpoint…
Studying Confirmation Bias in Hashtag Usage on Twitter
Dominik Kowald, Elisabeth Lex
The micro-blogging platform Twitter allows its nearly 320 million monthly active users to build a network of follower connections to other Twitter users (i.e., followees) in order…
Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations
Emanuel Lacic, Dominik Kowald, Elisabeth Lex
In this paper, we present work-in-progress on applying user pre-filtering to speed up and enhance recommendations based on Collaborative Filtering. We propose to pre-filter users i…
AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments
Dominik Kowald, Emanuel Lacic, Dieter Theiler +1
In this paper, we present preliminary results of AFEL-REC, a recommender system for social learning environments. AFEL-REC is build upon a scalable software architecture to provide…
Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence
Tomislav Duricic, Emanuel Lacic, Dominik Kowald +1
User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose…
Overcoming the Imbalance Between Tag Recommendation Approaches and Real-World Folksonomy Structures with Cognitive-Inspired Algorithms
Dominik Kowald, Elisabeth Lex
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 algor…