Bundle Recommendation (BR) aims at recommending bundled items on online content or e-commerce platform, such as song lists on a music platform or book lists on a reading website. Several graph based models have achieved state-of-the-art performance on BR task. But their performance is still sub-optimal, since the data sparsity problem tends to be more severe in real bundle recommendation scenarios, which limits graph-based models from more sufficient learning. In this paper, we propose a novel graph learning paradigm called Counterfactual Learning for Bundle Recommendation (CLBR) to mitigate the impact of data sparsity problem and improve bundle recommendation. Our paradigm consists of two main parts: counterfactual data augmentation and counterfactual constraint. The main idea of our paradigm lies in answering the counterfactual questions: "What would a user interact with if his/her interaction history changes?" "What would a user interact with if the bundle-item affiliation relations change?" In counterfactual data augmentation, we design a heuristic sampler to generate counterfactual graph views for graph-based models, which has better noise controlling than the stochastic sampler. We further propose counterfactual loss to constrain model learning for mitigating the effects of residual noise in augmented data and achieving more sufficient model optimization. Further theoretical analysis demonstrates the rationality of our design. Extensive experiments of BR models applied with our paradigm on two real-world datasets are conducted to verify the effectiveness of the paradigm
翻译:捆绑建议 (BR) 旨在推荐在线内容或电子商务平台上的捆绑项目, 如音乐平台或阅读网站上的书籍列表中的歌曲列表。 几个基于图形的模型已经实现了BR任务的最新表现。 但是,它们的性能仍然不尽理想, 因为数据宽度问题在真正的捆绑建议情景中往往更为严重, 从而限制了基于图形的模型, 从而限制了更充分的学习。 在本文中, 我们提议了一个新型图表学习模式, 名为“ 反事实学习 ” ( CLBR), 以缓解数据宽放问题的影响, 并改进捆绑建议。 我们的模式由两个主要部分组成: 反事实数据增强和反事实限制。 我们的范式的主要理念在于回答反事实问题 : “ 如果一个用户的交互历史变化会更加严重, 数据宽广度问题会更加严重。 ” “ 如果包绑项目关联关系改变? ” 用户会与什么互动? 反事实数据增强, 我们设计了一个超理论的样本, 来为基于图表的模型产生反事实的图形观点观点, 其噪音控制能力比抽样取样样本要好得多。 我们进一步提出“ ” 将进一步的理论优化分析 。