Learning Fair Representations for Bipartite Graph based Recommendation

by   Le Wu, et al.

As a key application of artificial intelligence, recommender systems are among the most pervasive computer aided systems to help users find potential items of interests. Recently, researchers paid considerable attention to fairness issues for artificial intelligence applications. Most of these approaches assumed independence of instances, and designed sophisticated models to eliminate the sensitive information to facilitate fairness. However, recommender systems differ greatly from these approaches as users and items naturally form a user-item bipartite graph, and are collaboratively correlated in the graph structure. In this paper, we propose a novel graph based technique for ensuring fairness of any recommendation models. Here, the fairness requirements refer to not exposing sensitive feature set in the user modeling process. Specifically, given the original embeddings from any recommendation models, we learn a composition of filters that transform each user's and each item's original embeddings into a filtered embedding space based on the sensitive feature set. For each user, this transformation is achieved under the adversarial learning of a user-centric graph, in order to obfuscate each sensitive feature between both the filtered user embedding and the sub graph structures of this user. Finally, extensive experimental results clearly show the effectiveness of our proposed model for fair recommendation. We publish the source code at https://github.com/newlei/FairGo.



page 1

page 2

page 3

page 4


Privileged Graph Distillation for Cold Start Recommendation

The cold start problem in recommender systems is a long-standing challen...

Exploring User Opinions of Fairness in Recommender Systems

Algorithmic fairness for artificial intelligence has become increasingly...

GLIMG: Global and Local Item Graphs for Top-N Recommender Systems

Graph-based recommendation models work well for top-N recommender system...

Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach

In many recommender systems, users and items are associated with attribu...

Recommender Systems Fairness Evaluation via Generalized Cross Entropy

Fairness in recommender systems has been considered with respect to sens...

Lightweight Compositional Embeddings for Incremental Streaming Recommendation

Most work in graph-based recommender systems considers a static setting ...

Quaternion-Based Graph Convolution Network for Recommendation

Graph Convolution Network (GCN) has been widely applied in recommender s...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.