Rehman Amjad, Ahmed Adeel, Alahmadi Tahani Jaser, Mirdad Abeer Rashad, Al Ghofaily Bayan, Saleem Khalid
Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University, Riyadh, Saudi Arabia.
Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan.
PeerJ Comput Sci. 2024 Oct 31;10:e2386. doi: 10.7717/peerj-cs.2386. eCollection 2024.
Rapid advancement in information technology promotes the growth of new online learning communities in an e-learning environment that overloads information and data sharing. When a new learner asks a question, how a system recommends the answer is the problem of the learner's cold start. In this article, our contributions are: (i) We proposed a Trust-aware Deep Neural Recommendation (TDNR) framework that addresses learner cold-start issues in informal e-learning by modeling complex nonlinear relationships. (ii) We utilized latent Dirichlet allocation for tag modeling, assigning tag categories to newly posted questions and ranking experts related to specific tags for active questioners based on hub and authority scores. (iii) We enhanced recommendation accuracy in the TDNR model by introducing a degree of trust between questioners and responders. (iv) We incorporated the questioner-responder relational graph, derived from structural preference information, into our proposed model. We evaluated the proposed model on the Stack Overflow dataset using mean absolute precision (MAP), root mean squared error (RMSE), and F-measure metrics. Our significant findings are that TDNR is a hybrid approach that provides more accurate recommendations compared to rating-based and social-trust-based approaches, the proposed model can facilitate the formation of informal e-learning communities, and experiments show that TDNR outperforms the competing methods by an improved margin. The model's robustness, demonstrated by superior MAE, RMSE, and F-measure metrics, makes it a reliable solution for addressing information overload and user sparsity in Stack Overflow. By accurately modeling complex relationships and incorporating trust degrees, TDNR provides more relevant and personalized recommendations, even in cold-start scenarios. This enhances user experience by facilitating the formation of supportive learning communities and ensuring new learners receive accurate recommendations.
信息技术的快速发展推动了电子学习环境中新型在线学习社区的成长，这种环境存在信息过载和数据共享的问题。当新学习者提出问题时，系统如何推荐答案就是学习者冷启动的问题。在本文中，我们的贡献如下：（i）我们提出了一种信任感知深度神经推荐（TDNR）框架，通过对复杂的非线性关系进行建模来解决非正式电子学习中的学习者冷启动问题。（ii）我们利用潜在狄利克雷分配进行标签建模，为新发布的问题分配标签类别，并根据中心度和权威分数为活跃提问者对与特定标签相关的专家进行排名。（iii）我们通过引入提问者和回答者之间的信任度来提高TDNR模型中的推荐准确性。（iv）我们将从结构偏好信息中衍生出的提问者 - 回答者关系图纳入我们提出的模型。我们使用平均绝对精度（MAP）、均方根误差（RMSE）和F值指标在Stack Overflow数据集上对提出的模型进行了评估。我们的重要发现是，TDNR是一种混合方法，与基于评级和基于社会信任的方法相比，它能提供更准确的推荐，所提出的模型可以促进非正式电子学习社区的形成，并且实验表明TDNR比竞争方法有更大的改进幅度。该模型通过卓越的平均绝对误差（MAE）、均方根误差（RMSE）和F值指标所展示的稳健性，使其成为解决Stack Overflow中信息过载和用户稀疏性问题的可靠解决方案。通过准确地对复杂关系进行建模并纳入信任度，TDNR即使在冷启动场景下也能提供更相关和个性化的推荐。这通过促进支持性学习社区的形成并确保新学习者获得准确的推荐来提升用户体验。
