Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

30 Mar 2020Tomislav DuricicHussain HussainEmanuel LacicDominik KowaldDenis HelicElisabeth Lex

In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering. In a cold-start setting, on three publicly available datasets, we evaluate approaches from four method families: (i) factorization-based, (ii) random walk-based, (iii) deep learning-based, and (iv) the Large-scale Information Network Embedding (LINE) approach... (read more)

PDF Abstract


No code implementations yet. Submit your code now

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.