通过Deconfounder减轻下一篮子推荐中的频率偏差
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders
摘要：
最近的下一个购物篮推荐（NBR）的研究取得了很大的进展，利用个性化的项目频率（PIF）的主要特征之一，它衡量用户的互动与项目的频率。然而，将PIF作为显式特征会导致对频繁项的偏见。用户频繁购买的项目在基于PIF的推荐系统中被分配更高的权重，并且更频繁地出现在个性化推荐列表中。结果，系统将失去用户频繁购买的物品和用户从不购买的物品之间的公平和平衡。我们将这种个性化推荐列表的系统偏差称为频率偏差，它缩小了用户的浏览范围，降低了系统的效用。我们采用因果推理理论来解决这个问题。考虑到历史购买对用户未来兴趣的影响，用户和物品表示可以被视为因果图中的未观察到的混杂因素。在本文中，我们提出了一个deconfounder模型命名为FENDER（频率感知Deconfounder下一个篮子推荐），以减轻频率偏差。利用我们提出的去混淆理论和因果图，FENDER用神经张量层分解PIF，以获得用户和项目的替代混淆因素。然后，FENDER考虑这些替代混杂因素的影响，执行无偏倚的建议。实验结果表明，与三个数据集上的十个基线模型相比，FENDER获得了多样化和公平的结果，同时实现了具有竞争力的性能。进一步的实验说明了FENDER如何平衡用户的历史购买和潜在兴趣。
英文摘要：
Recent studies on Next-basket Recommendation (NBR) have achieved much progress by leveraging Personalized Item Frequency (PIF) as one of the main features, which measures the frequency of the user's interactions with the item. However, taking the PIF as an explicit feature incurs bias towards frequent items. Items that a user purchases frequently are assigned higher weights in the PIF-based recommender system and appear more frequently in the personalized recommendation list. As a result, the system will lose the fairness and balance between items that the user frequently purchases and items that the user never purchases. We refer to this systematic bias on personalized recommendation lists as frequency bias, which narrows users' browsing scope and reduces the system utility. We adopt causal inference theory to address this issue. Considering the influence of historical purchases on users' future interests, the user and item representations can be viewed as unobserved confounders in the causal diagram. In this paper, we propose a deconfounder model named FENDER (Frequency-aware Deconfounder for Next-basket Recommendation) to mitigate the frequency bias. With the deconfounder theory and the causal diagram we propose, FENDER decomposes PIF with a neural tensor layer to obtain substitute confounders for users and items. Then, FENDER performs unbiased recommendations considering the effect of these substitute confounders. Experimental results demonstrate that FENDER has derived diverse and fair results compared to ten baseline models on three datasets while achieving competitive performance. Further experiments illustrate how FENDER balances users' historical purchases and potential interests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
