Modern recommender systems often deal with a variety of user interactions,
e.g., click, forward, purchase, etc., which requires the underlying recommender
engines to fully understand and leverage multi-behavior data from users.
Despite recent efforts towards making use of heterogeneous data, multi-behavior
recommendation still faces great challenges. Firstly, sparse target signals and
noisy auxiliary interactions remain an issue. Secondly, existing methods
utilizing self-supervised learning (SSL) to tackle the data sparsity neglect
the serious optimization imbalance between the SSL task and the target task.
Hence, we propose a Multi-Behavior Self-Supervised Learning (MBSSL) framework
together with an adaptive optimization method. Specifically, we devise a
behavior-aware graph neural network incorporating the self-attention mechanism
to capture behavior multiplicity and dependencies. To increase the robustness
to data sparsity under the target behavior and noisy interactions from
auxiliary behaviors, we propose a novel self-supervised learning paradigm to
conduct node self-discrimination at both inter-behavior and intra-behavior
levels. In addition, we develop a customized optimization strategy through
hybrid manipulation on gradients to adaptively balance the self-supervised
learning task and the main supervised recommendation task. Extensive
experiments on five real-world datasets demonstrate the consistent improvements
obtained by MBSSL over ten state-of-the art (SOTA) baselines. We release our
model implementation at: this https URL

本研究提出一种多行为自监督学习（MBSSL）框架，包括一种自我注意力机制和行为感知图神经网络，该框架可用于应对稀疏目标信号和嘈杂的辅助交互。此外，通过梯度的混合操作，开发了一种自适应优化策略，并在五个真实世界的数据集上对该方法进行了广泛的实验.

面向推荐的多行为自监督学习

Multi-behavior Self-supervised Learning for Recommendation

Most existing recommender systems leverage user behavior data of one type
only, such as the purchase behavior in E-commerce that is directly related to
the business KPI (Key Performance Indicator) of conversion rate. Besides the
key behavioral data, we argue that other forms of user behaviors also provide
valuable signal, such as views, clicks, adding a product to shop carts and so
on. They should be taken into account properly to provide quality
recommendation for users. In this work, we contribute a new solution named NMTR
(short for Neural Multi-Task Recommendation) for learning recommender systems
from user multi-behavior data. We develop a neural network model to capture the
complicated and multi-type interactions between users and items. In particular,
our model accounts for the cascading relationship among different types of
behaviors (e.g., a user must click on a product before purchasing it). To fully
exploit the signal in the data of multiple types of behaviors, we perform a
joint optimization based on the multi-task learning framework, where the
optimization on a behavior is treated as a task. Extensive experiments on two
real-world datasets demonstrate that NMTR significantly outperforms
state-of-the-art recommender systems that are designed to learn from both
single-behavior data and multi-behavior data. Further analysis shows that
modeling multiple behaviors is particularly useful for providing recommendation
for sparse users that have very few interactions.

本文介绍了一种名为 NMTR 的新型解决方案，其可以从多种类型的用户行为数据中学习推荐系统，并且在真实数据集上的实验表明，它明显优于其他现有的推荐系统，特别适用于缺乏数据交互的稀疏用户。