Cross-Domain Recommendation (CDR) seeks to enable effective knowledge
transfer across domains. Existing works rely on either representation alignment
or transformation bridges, but they struggle on identifying domain-shared from
domain-specific latent factors. Specifically, while CDR describes user
representations as a joint distribution over two domains, these methods fail to
account for its joint identifiability as they primarily fixate on the marginal
distribution within a particular domain. Such a failure may overlook the
conditionality between two domains and how it contributes to latent factor
disentanglement, leading to negative transfer when domains are weakly
correlated. In this study, we explore what should and should not be transferred
in cross-domain user representations from a causality perspective. We propose a
Hierarchical subspace disentanglement approach to explore the Joint
IDentifiability of cross-domain joint distribution, termed HJID, to preserve
domain-specific behaviors from domain-shared factors. HJID organizes user
representations into layers: generic shallow subspaces and domain-oriented deep
subspaces. We first encode the generic pattern in the shallow subspace by
minimizing the Maximum Mean Discrepancy of initial layer activation. Then, to
dissect how domain-oriented latent factors are encoded in deeper layers
activation, we construct a cross-domain causality-based data generation graph,
which identifies cross-domain consistent and domain-specific components,
adhering to the Minimal Change principle. This allows HJID to maintain
stability whilst discovering unique factors for different domains, all within a
generative framework of invertible transformations that guarantee the joint
identifiability. With experiments on real-world datasets, we show that HJID
outperforms SOTA methods on a range of strongly and weakly correlated CDR
tasks.

为有效实现跨领域知识传递，本研究从因果学的角度出发，提出一种分层子空间解缠方法（HJID），以探索跨领域联合分布的联合识别性，并保留特定领域行为和域共享要素。实验证明，HJID 在一系列强相关和弱相关跨领域推荐任务中优于现有方法。

通过层次子空间解缠交叉领域推荐的联合可辨识性

Joint Identifiability of Cross-Domain Recommendation via Hierarchical  Subspace Disentanglement

In the pursuit of transferring a source model to a target domain without
access to the source training data, Source-Free Domain Adaptation (SFDA) has
been extensively explored across various scenarios, including closed-set,
open-set, partial-set, and generalized settings. Existing methods, focusing on
specific scenarios, not only address only a subset of challenges but also
necessitate prior knowledge of the target domain, significantly limiting their
practical utility and deployability. In light of these considerations, we
introduce a more practical yet challenging problem, termed unified SFDA, which
comprehensively incorporates all specific scenarios in a unified manner. To
tackle this unified SFDA problem, we propose a novel approach called Latent
Causal Factors Discovery (LCFD). In contrast to previous alternatives that
emphasize learning the statistical description of reality, we formulate LCFD
from a causality perspective. The objective is to uncover the causal
relationships between latent variables and model decisions, enhancing the
reliability and robustness of the learned model against domain shifts. To
integrate extensive world knowledge, we leverage a pre-trained vision-language
model such as CLIP. This aids in the formation and discovery of latent causal
factors in the absence of supervision in the variation of distribution and
semantics, coupled with a newly designed information bottleneck with
theoretical guarantees. Extensive experiments demonstrate that LCFD can achieve
new state-of-the-art results in distinct SFDA settings, as well as source-free
out-of-distribution generalization.Our code and data are available at
this https URL

统一的无源领域适应问题中，通过采用潜在因果因素发现方法（LCFD）和预训练的视觉 - 语言模型（如 CLIP），可以有效提高学习模型对领域变化的可靠性和鲁棒性，并在不同的无源领域适应设置和无源领域外泛化上取得了新的最先进结果。

统一无源领域适应

Unified Source-Free Domain Adaptation

Knowledge tracing (KT) aims to monitor students' evolving knowledge states
through their learning interactions with concept-related questions, and can be
indirectly evaluated by predicting how students will perform on future
questions. In this paper, we observe that there is a common phenomenon of
answer bias, i.e., a highly unbalanced distribution of correct and incorrect
answers for each question. Existing models tend to memorize the answer bias as
a shortcut for achieving high prediction performance in KT, thereby failing to
fully understand students' knowledge states. To address this issue, we approach
the KT task from a causality perspective. A causal graph of KT is first
established, from which we identify that the impact of answer bias lies in the
direct causal effect of questions on students' responses. A novel
COunterfactual REasoning (CORE) framework for KT is further proposed, which
separately captures the total causal effect and direct causal effect during
training, and mitigates answer bias by subtracting the latter from the former
in testing. The CORE framework is applicable to various existing KT models, and
we implement it based on the prevailing DKT, DKVMN, and AKT models,
respectively. Extensive experiments on three benchmark datasets demonstrate the
effectiveness of CORE in making the debiased inference for KT.

知识追踪（KT）旨在通过学生与概念相关问题的学习互动来监控他们不断变化的知识状态，并可通过预测学生在未来问题上的表现来间接评估。这篇论文观察到了存在一个普遍的答案偏差现象，即每个问题的正确和错误答案呈高度不平衡的分布。针对这个问题，作者从因果关系的角度处理了 KT 任务，提出了一种新颖的核心（CORE）框架来减轻答案偏差，并通过从总因果效应中减去直接因果效应来进行 debias 推理。该 CORE 框架适用于现有的多种 KT 模型，并在三个基准数据集上的大量实验证明了它在进行知识追踪时的有效性。