As an emerging education strategy, learnersourcing offers the potential for
personalized learning content creation, but also grapples with the challenge of
predicting student performance due to inherent noise in student-generated data.
While graph-based methods excel in capturing dense learner-question
interactions, they falter in cold start scenarios, characterized by limited
interactions, as seen when questions lack substantial learner responses. In
response, we introduce an innovative strategy that synergizes the potential of
integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM)
embeddings. Our methodology employs a signed bipartite graph to comprehensively
model student answers, complemented by a contrastive learning framework that
enhances noise resilience. Furthermore, LLM's contribution lies in generating
foundational question embeddings, proving especially advantageous in addressing
cold start scenarios characterized by limited graph data interactions.
Validation across five real-world datasets sourced from the PeerWise platform
underscores our approach's effectiveness. Our method outperforms baselines,
showcasing enhanced predictive accuracy and robustness.

通过集成 SGNN 和 LLM 嵌入的创新策略来增强学生问答数据的噪声韧性，从而提高基于学习者提供的数据的预测准确性。在冷启动场景下表现出优异的预测准确性，尤其有利于解决问题缺乏学习者反馈的情况。经过对 5 个实际数据集的验证，方法胜过基准方法，展示了增强的预测准确性和鲁棒性。

提高学生性能预测中基于学习者提供问题的 SGNN-LLM 协同效应

Enhancing Student Performance Prediction on Learnersourced Questions  with SGNN-LLM Synergy

In modern commercial search engines and recommendation systems, data from
multiple domains is available to jointly train the multi-domain model.
Traditional methods train multi-domain models in the multi-task setting, with
shared parameters to learn the similarity of multiple tasks, and task-specific
parameters to learn the divergence of features, labels, and sample
distributions of individual tasks. With the development of large language
models, LLM can extract global domain-invariant text features that serve both
search and recommendation tasks. We propose a novel framework called S\&R
Multi-Domain Foundation, which uses LLM to extract domain invariant features,
and Aspect Gating Fusion to merge the ID feature, domain invariant text
features and task-specific heterogeneous sparse features to obtain the
representations of query and item. Additionally, samples from multiple search
and recommendation scenarios are trained jointly with Domain Adaptive
Multi-Task module to obtain the multi-domain foundation model. We apply the
S\&R Multi-Domain foundation model to cold start scenarios in the
pretrain-finetune manner, which achieves better performance than other SOTA
transfer learning methods. The S\&R Multi-Domain Foundation model has been
successfully deployed in Alipay Mobile Application's online services, such as
content query recommendation and service card recommendation, etc.

在现代商业搜索引擎和推荐系统中，数据来自多个领域被用于联合训练多领域模型。使用大型语言模型，提出了一种名为 S&R Multi-Domain Foundation 的新型框架，其中利用 LLM 提取领域不变的文本特征，并使用 Aspect Gating Fusion 将 ID 特征、领域不变的文本特征和任务特异性稀疏特征合并以获得查询和项的表示。同时，使用领域自适应多任务模块联合训练来自多个搜索和推荐场景的样本，获得多领域基础模型。将 S&R Multi-Domain Foundation 模型应用于冷启动场景中，以 pretrain-finetune 方式取得比其他 SOTA 迁移学习方法更好的性能。S&R Multi-Domain Foundation 模型已成功部署在支付宝移动应用的在线服务中，如内容查询推荐和服务卡片推荐等。