ConGCN: Factorized Graph Convolutional Networks for Consensus Recommendation

Boyu Li, Ting Guo, Xingquan Zhu, Yang Wang, Fang Chen

Abstract: An essential weakness of existing personalized recommender systems is that the learning is biased and dominated by popular items and users. Existing methods, particularly graph-based approaches, primarily focus on the “heterogeneous interaction” between user-item, leading to a disproportionately large influence of popular nodes during the graph learning process. Recently, popularity debiasing models have been proposed to address this issue, but they excessively concentrate on considering cause-effect or re-weighting the item/user popularity. These approaches artificially alter the nature of the data, inadvertently downplaying the representation learning of popular items/users. Consequently, balancing the trade-off between global recommendation accuracy and unpopular items/users exposure is challenging.