Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

03/30/2020
by   Tomislav Duricic, et al.
0

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. We find that across the four families, random-walk-based approaches consistently achieve the best accuracy. Besides, they result in highly novel and diverse recommendations. Furthermore, our results show that the use of graph embeddings in trust-based collaborative filtering significantly improves user coverage.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/18/2018

Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence

User-based Collaborative Filtering (CF) is one of the most popular appro...
research
04/16/2013

Efficient Computation of Mean Truncated Hitting Times on Very Large Graphs

Previous work has shown the effectiveness of random walk hitting times a...
research
08/11/2017

iTrace: An Implicit Trust Inference Method for Trust-aware Collaborative Filtering

The growth of Internet commerce has stimulated the use of collaborative ...
research
01/10/2013

Using Temporal Data for Making Recommendations

We treat collaborative filtering as a univariate time series estimation ...
research
09/26/2013

One-class Collaborative Filtering with Random Graphs: Annotated Version

The bane of one-class collaborative filtering is interpreting and modell...
research
09/16/2022

The effectiveness of factorization and similarity blending

Collaborative Filtering (CF) is a widely used technique which allows to ...
research
09/09/2019

Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks

Item-based models are among the most popular collaborative filtering app...

Please sign up or login with your details

Forgot password? Click here to reset