Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2019

Kowald Dominik, Lacic Emanuel, Theiler Dieter, Traub Matthias, Kuffer Lucky, Lindstaedt Stefanie , Lex Elisabeth

Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metrik

REVEAL Workshop co-located with RecSys'2019, ACM, Kopenhagen, Denmark, 2019

Konferenz
2019

Kowald Dominik, Traub Matthias, Theiler Dieter, Gursch Heimo, Lacic Emanuel, Lindstaedt Stefanie , Kern Roman, Lex Elisabeth

Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets

REVEAL Workshop co-located with RecSys'2019, ACM, 2019

Konferenz
2019

Duricic Tomislav, Lacic Emanuel, Kowald Dominik, Lex Elisabeth

Exploiting weak ties in trust-based recommender systems using regular equivalence

EUROCSS'2019, 2019

Konferenz
User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. CF, however, suffers from data sparsity and the cold-start problem since users often rate only a small fraction of available items. One solution is to incorporate additional information into the recommendation process such as explicit trust scores that are assigned by users to others or implicit trust relationships that result from social connections between users. Such relationships typically form a very sparse trust network, which can be utilized to generate recommendations for users based on people they trust. In our work, we explore the use of regular equivalence applied to a trust network to generate a similarity matrix that is used for selecting k-nearest neighbors used for item recommendation. Two vertices in a network are regularly equivalent if their neighbors are themselves equivalent and by using the iterative approach of calculating regular equivalence, we can study the impact of strong and weak ties on item recommendation. We evaluate our approach on cold start users on a dataset crawled from Epinions and find that by using weak ties in addition to strong ties, we can improve the performance of a trust-based recommender in terms of recommendation accuracy.
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