Abstract
Graph neural networks (GNNs) have significantly advanced recommender systems (RecSys) by enhancing their accuracy in complex collaborative filtering scenarios. However, this progress often comes at the cost of overlooking the diversity of recommendations, a factor in user satisfaction. Addressing this gap, this paper introduces the disentangled representation graph neural network (DRGNN). DRGNN integrates diversification into the candidate generation stage using two specialized modules. The first employs disentangled representation learning to separate item preferences from category preferences, thereby mitigating category bias in recommendations. The second module, focusing on positive sample selection, further reduces category bias. This approach not only maintains the high-order connectivity strengths of GNNs but also substantially improves the diversity of recommendations. Our extensive validation of DRGNN on three comprehensive web service datasets, Taobao, Amazon Beauty and MSD, shows that it not only matches the state-of-the-art methods in accuracy but also excels in achieving a balanced trade-off between accuracy and diversity in recommendations.
Published Version
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