  Cross-Domain Recommendation (CDR) is an effective way to alleviate the
cold-start problem. However, previous work severely ignores fairness and bias
when learning the mapping function, which is used to obtain the representations
for fresh users in the target domain. To study this problem, in this paper, we
propose a Fairness-aware Cross-Domain Recommendation model, called FairCDR. Our
method achieves user-oriented group fairness by learning the fairness-aware
mapping function. Since the overlapping data are quite limited and
distributionally biased, FairCDR leverages abundant non-overlapping users and
interactions to help alleviate these problems. Considering that each individual
has different influence on model fairness, we propose a new reweighing method
based on Influence Function (IF) to reduce unfairness while maintaining
recommendation accuracy. Extensive experiments are conducted to demonstrate the
effectiveness of our model.

Information Retrieval