Recommender systems usually learn user interests from various user behaviors,
including clicks and post-click behaviors (e.g., like and favorite). However,
these behaviors inevitably exhibit popularity bias, leading to some unfairness
issues: 1) for items with similar quality, more popular ones get more exposure;
and 2) even worse the popular items with lower popularity might receive more
exposure. Existing work on mitigating popularity bias blindly eliminates the
bias and usually ignores the effect of item quality. We argue that the
relationships between different user behaviors (e.g., conversion rate) actually
reflect the item quality. Therefore, to handle the unfairness issues, we
propose to mitigate the popularity bias by considering multiple user behaviors.
In this work, we examine causal relationships behind the interaction
generation procedure in multi-behavior recommendation. Specifically, we find
that: 1) item popularity is a confounder between the exposed items and users'
post-click interactions, leading to the first unfairness; and 2) some hidden
confounders (e.g., the reputation of item producers) affect both item
popularity and quality, resulting in the second unfairness. To alleviate these
confounding issues, we propose a causal framework to estimate the causal
effect, which leverages backdoor adjustment to block the backdoor paths caused
by the confounders. In the inference stage, we remove the negative effect of
popularity and utilize the good effect of quality for recommendation.
Experiments on two real-world datasets validate the effectiveness of our
proposed framework, which enhances fairness without sacrificing recommendation
accuracy.

本文提出了一种基于因果框架、通过多种用户行为数据进行推荐，以消除流行度偏差并提高推荐公平性且不牺牲推荐准确性的方法。

多行为推荐中的公平因果干预

Causal Intervention for Fairness in Multi-behavior Recommendation

Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.)
widely exist in most real-world recommendation scenarios, which are beneficial
to learn users' multi-faceted preferences. As dependencies are explicitly
exhibited by the multiple types of behaviors, effectively modeling complex
behavior dependencies is crucial for multi-behavior prediction. The
state-of-the-art multi-behavior models learn behavior dependencies
indistinguishably with all historical interactions as input. However, different
behaviors may reflect different aspects of user preference, which means that
some irrelevant interactions may play as noises to the target behavior to be
predicted. To address the aforementioned limitations, we introduce
multi-interest learning to the multi-behavior recommendation. More
specifically, we propose a novel Coarse-to-fine Knowledge-enhanced
Multi-interest Learning (CKML) framework to learn shared and behavior-specific
interests for different behaviors. CKML introduces two advanced modules, namely
Coarse-grained Interest Extracting (CIE) and Fine-grained Behavioral
Correlation (FBC), which work jointly to capture fine-grained behavioral
dependencies. CIE uses knowledge-aware information to extract initial
representations of each interest. FBC incorporates a dynamic routing scheme to
further assign each behavior among interests. Additionally, we use the
self-attention mechanism to correlate different behavioral information at the
interest level. Empirical results on three real-world datasets verify the
effectiveness and efficiency of our model in exploiting multi-behavior data.
Further experiments demonstrate the effectiveness of each module and the
robustness and superiority of the shared and specific modelling paradigm for
multi-behavior data.

本文介绍了一种基于粗细粒度知识增强的多兴趣学习框架，用于在多行为推荐中学习共享和特定的行为兴趣，通过使用两个先进的模块收集和分配相应的行为兴趣，并运用自我关注机制在兴趣层次上将不同的行为信息相互关联。实证结果表明了我们的模型在利用多行为数据方面的效果和效率，以及共享和特定建模范式的鲁棒性和优越性。

用于多行为推荐的粗到细知识增强多兴趣学习框架

Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation

Multi-behavior recommendation (MBR) aims to jointly consider multiple
behaviors to improve the target behavior's performance. We argue that MBR
models should: (1) model the coarse-grained commonalities between different
behaviors of a user, (2) consider both individual sequence view and global
graph view in multi-behavior modeling, and (3) capture the fine-grained
differences between multiple behaviors of a user. In this work, we propose a
novel Multi-behavior Multi-view Contrastive Learning Recommendation (MMCLR)
framework, including three new CL tasks to solve the above challenges,
respectively. The multi-behavior CL aims to make different user single-behavior
representations of the same user in each view to be similar. The multi-view CL
attempts to bridge the gap between a user's sequence-view and graph-view
representations. The behavior distinction CL focuses on modeling fine-grained
differences of different behaviors. In experiments, we conduct extensive
evaluations and ablation tests to verify the effectiveness of MMCLR and various
CL tasks on two real-world datasets, achieving SOTA performance over existing
baselines. Our code will be available on
https://github.com/wyqing20/MMCLR

提出一种新的多行为多视角对比学习推荐算法，其中包括三种新的对比学习任务，可解决多行为建模中的挑战，在两个真实数据集的广泛评估和消融测试中，该方法取得了超越现有基线的 SOTA 性能。

Multi-view Multi-behavior Contrastive Learning in Recommendation

Recommender systems have played a critical role in many web applications to
meet user's personalized interests and alleviate the information overload. In
this survey, we review the development of recommendation frameworks with the
focus on heterogeneous relational learning, which consists of different types
of dependencies among users and items. The objective of this task is to map
heterogeneous relational data into latent representation space, such that the
structural and relational properties from both user and item domain can be well
preserved. To address this problem, recent research developments can fall into
three major lines: social recommendation, knowledge graph-enhanced recommender
system, and multi-behavior recommendation. We discuss the learning approaches
in each category, such as matrix factorization, attention mechanism and graph
neural networks, for effectively distilling heterogeneous contextual
information. Finally, we present an exploratory outlook to highlight several
promising directions and opportunities in heterogeneous relational learning
frameworks for recommendation.

调查了不同类型的推荐框架的发展，重点关注于异构关系学习，分类研究了社交推荐、知识图增强推荐系统以及多行为推荐的学习方法，探讨如何有效地提取异构上下文信息。最后对异构关系学习提出了新的方向和机会。