Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this article, we propose a deep learning (DL) based collaborative filtering (CF) framework, namely, deep matrix factorization (DMF), which can integrate any kind of side information effectively and handily. In DMF, two feature transforming functions are built to directly generate latent factors of users and items from various input information. As for the implicit feedback that is commonly used as input of recommendation algorithms, implicit feedback embedding (IFE) is proposed. IFE converts the high-dimensional and sparse implicit feedback information into a low-dimensional real-valued vector retaining primary features. Using IFE could reduce the scale of model parameters conspicuously and increase model training efficiency. Experimental results on five public databases indicate that the proposed method performs better than the state-of-the-art DL based recommendation algorithms on both accuracy and training efficiency in terms of quantitative assessments.

In a MOOC learning environment, it is essential to understand students' social knowledge constructs and critical thinking for instructors to design intervention strategies. The development of social knowledge constructs and critical thinking can be represented by cognitive presence, which is a primary component of the Community of Inquiry (CoI) model. However, identifying learners' cognitive presence is a challenging problem, and most researchers have performed this task using traditional machine learning methods that require both manual feature construction and adequate labeled data. In this paper, we present a novel variant of the Bidirectional Encoder Representations from Transformers (BERT) model for cognitive presence identification, namely MOOC-BERT, which is pre-trained on large-scale unlabeled discussion data collected from various MOOCs involving different disciplines. MOOC-BERT learned deep representations of unlabeled data and adopted Chinese characters as inputs without any feature engineering. The experimental results showed that MOOC-BERT outperformed the representative machine learning algorithms and deep learning models in the performance of identification and cross-course generalization. Then, MOOC-BERT was adopted to identify the unlabeled posts of the two courses. The empirical analysis results revealed the evolution and differences in MOOC learners' cognitive presence levels. These findings provide valuable insights into the effectiveness of pre-training on large-scale and multidiscipline discussion data in facilitating accurate cognitive presence identification, demonstrating the practical value of MOOC-BERT in learning analytics.

Material detection by industrial infrared imaging spectrometer (IRIS) is a key technique in multiple industrial applications, including garbage collection, material analysis, and robot vision. However, IRIS often suffers from overlapped bands and random noises, which limit the precision of subsequent processing. In this article, we propose a novel Gabor transform-based mid-wave infrared (MWIR) spectrum restoration model by successfully exploring the intrinsic structure of the clean MWIR spectrum from the degraded one. First, a total variation-regularized Gabor coefficient adjustment descriptor is designed and incorporated into the spectrum restoration model. In addition to adjusting the Gabor coefficient distribution by total variation-norm, the L2-norm of gradient is leveraged to regulate the smoothness of the instrument degradation function. Then, the proposed model is inferred using an efficient optimization approach based on split Bregman iteration method and alternating minimization algorithm. Quantitative and qualitative experimental results demonstrate that the proposed model favorably outperforms the state-of-the-art approaches. The restored high-resolution MWIR spectrum can be used to rapidly detect different materials in intelligent video systems.

Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this article, we have proposed a deep learning based fully Bayesian treatment recommendation framework, DVMF, which has high-quality performance and ability to integrate any kinds of side information handily and efficiently. In DVMF, the variational inference technique and the reparameterization tricks are introduced to make DVMF possible to be optimized by the stochastic gradient-based methods, in addition, two novel deep neural networks have been constructed to infer the hyper-parameters of the distributions of latent factors from the knowledge of user and item, which are represented as low-dimensional real-valued vectors retaining primary features. Experimental results on five public databases indicate that the proposed method performs better than the state-of-the-art recommendation algorithms on prediction accuracy in terms of quantitative assessments.

Identifying the epistemic emotions of learner-generated reviews in massive open online courses (MOOCs) can help instructors provide adaptive guidance and interventions for learners. The epistemic emotion identification task is a fine-grained identification task that contains multiple categories of emotions arising during the learning process. Previous studies only consider emotional or semantic information within the review texts alone, which leads to insufficient feature representation. In addition, some categories of epistemic emotions are ambiguously distributed in feature space, making them hard to be distinguished. In this article, we present an emotion-semantic-aware dual contrastive learning (ES-DCL) approach to tackle these issues. In order to learn sufficient feature representation, implicit semantic features and human-interpretable emotional features are, respectively, extracted from two different views to form complementary emotional-semantic features. On this basis, by leveraging the experience of domain experts and the input emotional-semantic features, two types of contrastive losses (label contrastive loss and feature contrastive loss) are formulated. They are designed to train the discriminative distribution of emotional-semantic features in the sample space and to solve the anisotropy problem between different categories of epistemic emotions. The proposed ES-DCL is compared with 11 other baseline models on four different disciplinary MOOCs review datasets. Extensive experimental results show that our approach improves the performance of epistemic emotion identification, and significantly outperforms state-of-the-art deep learning-based methods in learning more discriminative sentence representations.

Personalized e-learning systems are applications of consumer electronics in the field of education that provide individualized and adaptive services for users. Knowledge tracing (KT), as a key technology, aims to model learners’ knowledge states through the interactions between learners and learning resources, and predicts their future performance. However, the problem of interaction sparsity in educational resources leads to the fact that simple representations of questions usually fail to accurately capture students’ knowledge states. In this paper, inspired by the data-driven paradigm, we propose a novel knowledge tracing method named weighted heterogeneous graph-based Three-view Contrastive Learning framework for Knowledge Tracing (TCL4KT). Technically, three different view encoders in TCL4KT complement each other to obtain question embeddings with rich information. Specifically, TCL4KT considers the semantic information of higher-order, heterogeneous and the downstream task on a weighted heterogeneous graph of KT to learn high-quality representations. Besides, a meta-path-based positive sample selection strategy and joint contrastive loss are employed to gain better prediction performance. Experimental results on four datasets demonstrate the superiority of TCL4KT over baseline models, and further analysis verifies the effectiveness of our three-view contrastive learning framework.