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

Code


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.