16 citations · 38 across the 12 of their papers we have counts for
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cs.IR2019
Should we Embed? A Study on the Online Performance of Utilizing Embeddings for Real-Time Job Recommendations
Markus Reiter-Haas, Emanuel Lacic, Tomislav Duricic +2
In this work, we present the findings of an online study, where we explore the impact of utilizing embeddings to recommend job postings under real-time constraints. On the Austrian…
cs.SI2019
Exploiting weak ties in trust-based recommender systems 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. CF, however, suffers from data sparsity and the cold-start problem sinc…