Modeling users' dynamic preferences from historical behaviors lies at the
core of modern recommender systems. Due to the diverse nature of user
interests, recent advances propose the multi-interest networks to encode
historical behaviors into multiple interest vectors. In real scenarios, the
corresponding items of captured interests are usually retrieved together to get
exposure and collected into training data, which produces dependencies among
interests. Unfortunately, multi-interest networks may incorrectly concentrate
on subtle dependencies among captured interests. Misled by these dependencies,
the spurious correlations between irrelevant interests and targets are
captured, resulting in the instability of prediction results when training and
test distributions do not match. In this paper, we introduce the widely used
Hilbert-Schmidt Independence Criterion (HSIC) to measure the degree of
independence among captured interests and empirically show that the continuous
increase of HSIC may harm model performance. Based on this, we propose a novel
multi-interest network, named DEep Stable Multi-Interest Learning (DESMIL),
which tries to eliminate the influence of subtle dependencies among captured
interests via learning weights for training samples and make model concentrate
more on underlying true causation. We conduct extensive experiments on public
recommendation datasets, a large-scale industrial dataset and the synthetic
datasets which simulate the out-of-distribution data. Experimental results
demonstrate that our proposed DESMIL outperforms state-of-the-art models by a
significant margin. Besides, we also conduct comprehensive model analysis to
reveal the reason why DESMIL works to a certain extent.

本研究借助 Hilbert-Schmidt 独立性准则提出了一种名为 Deep Stable Multi-Interest Learning (DESMIL) 的新型多兴趣网络，旨在通过为训练样本学习权重以消除捕获兴趣之间微妙依赖的影响，更多地关注底层真正的因果关系，并在公共推荐数据集，大规模工业数据集和模拟分布外数据的合成数据集上进行了大量实验，结果表明我们提出的 DESMIL 模型显著优于现有技术。