Negative sampling plays a crucial role in training successful sequential
recommendation models. Instead of merely employing random negative sample
selection, numerous strategies have been proposed to mine informative negative
samples to enhance training and performance. However, few of these approaches
utilize structural information. In this work, we observe that as training
progresses, the distributions of node-pair similarities in different groups
with varying degrees of neighborhood overlap change significantly, suggesting
that item pairs in distinct groups may possess different negative
relationships. Motivated by this observation, we propose a Graph-based Negative
sampling approach based on Neighborhood Overlap (GNNO) to exploit structural
information hidden in user behaviors for negative mining. GNNO first constructs
a global weighted item transition graph using training sequences. Subsequently,
it mines hard negative samples based on the degree of overlap with the target
item on the graph. Furthermore, GNNO employs curriculum learning to control the
hardness of negative samples, progressing from easy to difficult. Extensive
experiments on three Amazon benchmarks demonstrate GNNO's effectiveness in
consistently enhancing the performance of various state-of-the-art models and
surpassing existing negative sampling strategies. The code will be released at
https://github.com/floatSDSDS/GNNO.

本文提出一种基于图结构和邻域重叠的负采样方法，通过课程学习控制负例难度，有效地提升基于序列推荐模型的训练与预测性能。

基于邻域的硬负样本挖掘用于序列推荐

Neighborhood-based Hard Negative Mining for Sequential Recommendation

Sequential recommendation models, models that learn from chronological
user-item interactions, outperform traditional recommendation models in many
settings. Despite the success of sequential recommendation models, their
robustness has recently come into question. Two properties unique to the nature
of sequential recommendation models may impair their robustness - the cascade
effects induced during training and the model's tendency to rely too heavily on
temporal information. To address these vulnerabilities, we propose
Cascade-guided Adversarial training, a new adversarial training procedure that
is specifically designed for sequential recommendation models. Our approach
harnesses the intrinsic cascade effects present in sequential modeling to
produce strategic adversarial perturbations to item embeddings during training.
Experiments on training state-of-the-art sequential models on four public
datasets from different domains show that our training approach produces
superior model ranking accuracy and superior model robustness to real item
replacement perturbations when compared to both standard model training and
generic adversarial training.

本文提出了 Cascade-guided Adversarial training，一种针对顺序推荐模型的新对抗性训练过程，以解决顺序推荐模型的健壮性和过度依赖时间信息的问题。实验结果表明，我们的训练方法在模型排名准确性和模型健壮性方面都优于标准模型训练和通用对抗性训练。

通过级联引导的对抗训练实现更强大和准确的顺序推荐

Towards More Robust and Accurate Sequential Recommendation with  Cascade-guided Adversarial Training

Cross domain recommender system constitutes a powerful method to tackle the
cold-start and sparsity problem by aggregating and transferring user
preferences across multiple category domains. Therefore, it has great potential
to improve click-through-rate prediction performance in online commerce
platforms having many domains of products. While several cross domain
sequential recommendation models have been proposed to leverage information
from a source domain to improve CTR predictions in a target domain, they did
not take into account bidirectional latent relations of user preferences across
source-target domain pairs. As such, they cannot provide enhanced cross-domain
CTR predictions for both domains simultaneously. In this paper, we propose a
novel approach to cross-domain sequential recommendations based on the dual
learning mechanism that simultaneously transfers information between two
related domains in an iterative manner until the learning process stabilizes.
In particular, the proposed Dual Attentive Sequential Learning (DASL) model
consists of two novel components Dual Embedding and Dual Attention, which
jointly establish the two-stage learning process: we first construct dual
latent embeddings that extract user preferences in both domains simultaneously,
and subsequently provide cross-domain recommendations by matching the extracted
latent embeddings with candidate items through dual-attention learning
mechanism. We conduct extensive offline experiments on three real-world
datasets to demonstrate the superiority of our proposed model, which
significantly and consistently outperforms several state-of-the-art baselines
across all experimental settings. We also conduct an online A/B test at a major
video streaming platform Alibaba-Youku, where our proposed model significantly
improves business performance over the latest production system in the company.

提出了一种基于双学习机制的跨领域用户行为建模方法，为两个相关领域建立双重的潜在嵌入，实现了在两个目标域上同时提高 CTR 预测性能的目标，实验证明了比其他最先进的模型表现更好的效率和性能。