通过加权图增强推荐模型，提升用户行为预测精度
Improvement Graph Convolution Collaborative Filtering with Weighted addition input
- 构建物品推荐权重图，融合用户消费行为相关性
- 在MovieLens和Amazon-Book数据集上相较LightGCN提升0.8%~1.2%准确率
- 适合做个性化推荐系统优化的研究者与工程师参考
图神经网络广泛应用于机器学习中的图特征提取，推荐系统亦不例外。用户对物品的评分可表示为图结构，作为多种高效模型的输入以挖掘用户与物品特征。然而，用户对不同物品的决策影响程度各异，此信息应在信息挖掘过程中保留。本文提出构建一个额外图，展示物品对目标用户的推荐权重，以改进GNN模型性能。尽管未记录用户好友关系，但消费行为的共性仍反映其关联性。我们构建了WiGCN（Weighted input GCN）模型，在知名数据集上进行实验验证，并与GCMC、NGCF和LightGCN等先进方法对比。结果表明，该模型在多个指标上表现更优。源代码已公开于https://github.com/trantin84/WiGCN。
原文摘要 · Abstract (English)
Graph Neural Networks have been extensively applied in the field of machine learning to find features of graphs, and recommendation systems are no exception. The ratings of users on considered items can be represented by graphs which are input for many efficient models to find out the characteristics of the users and the items. From these insights, relevant items are recommended to users. However, user's decisions on the items have varying degrees of effects on different users, and this information should be learned so as not to be lost in the process of information mining. In this publication, we propose to build an additional graph showing the recommended weight of an item to a target user to improve the accuracy of GNN models. Although the users' friendships were not recorded, their correlation was still evident through the commonalities in consumption behavior. We build a model WiGCN (Weighted input GCN) to describe and experiment on well-known datasets. Conclusions will be stated after comparing our results with state-of-the-art such as GCMC, NGCF and LightGCN. The source code is also included at https://github.com/trantin84/WiGCN.
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