Knowledge tracing, the goal of which is predicting students’ future performance given their past question response sequences to trace their knowledge states, is pivotal for computer-aided education and intelligent tutoring systems. Although many technical efforts have been devoted to modeling students based on their question-response sequences, fine-grained interaction modeling between question-response pairs within each sequence is underexplored. This causes question-response representations less contextualized and further limits student modeling. To address this issue, we first conduct a data analysis and reveal the existence of complex cross effects between different question-response pairs within a sequence. Consequently, we propose MRT-KT, a multi-relational transformer for knowledge tracing, to enable fine-grained interaction modeling between question-response pairs. It introduces a novel relation encoding scheme based on knowledge concepts and student performance. Comprehensive experimental results show that MRT-KT outperforms state-of-the-art knowledge tracing methods on four widely-used datasets, validating the effectiveness of considering fine-grained interaction for knowledge tracing.

Traditional Chinese Medicine (TCM) has the longest clinical history in Asia and contributes a lot to health maintenance worldwide. An essential step during the TCM diagnostic process is syndrome induction, which comprehensively analyzes the symptoms and generates an overall summary of the symptoms. Given a set of symptoms, the existing herb recommenders aim to generate the corresponding herbs as a treatment by inducing the implicit syndrome representations based on TCM prescriptions. As different symptoms have various importance during the comprehensive consideration, we argue that treating the co-occurred symptoms equally to do syndrome induction in the previous studies will lead to the coarse-grained syndrome representation. In this paper, we bring the attention mechanism to model the syndrome induction process. Given a set of symptoms, we leverage an attention network to discriminate the symptom importance and adaptively fuse the symptom embeddings. Besides, we introduce a TCM knowledge graph to enrich the input corpus and improve the quality of representation learning. Further, we build a KG-enhanced Multi-Graph Neural Network architecture, which performs the attentive propagation to combine node feature and graph structural information. Extensive experimental results on two TCM data sets show that our proposed model has the outstanding performance over the state-of-the-arts.

In this article, we investigate the problem of social link inference in a target location-aware social network (LSN), which aims at predicting the unobserved links between users within the network. This problem is critical for downstream applications, including network completion and friend recommendation. In addition to the network structures commonly used in general link prediction, the studies tailored for social link inference in an LSN leverage user trajectories from the spatial aspect. However, the temporal factor lying in user trajectories is largely overlooked by most of the prior studies, limiting the capabilities of capturing the temporal relevance between users. Moreover, effective user matching by fusing different views, i.e., social, spatial, and temporal factors, remains unresolved, which hinders the potential improvement of link inference. To this end, this article devises a novel multiview matching network (MVMN) by regarding each of the three factors as one view of any target user pair. MVMN enjoys the flexibility and completeness of modeling each factor by developing its suitable matching module: 1) location matching module; 2) time-series matching module; and 3) relation matching module. Each module learns a view-specific representation for matching, and MVMN fuses them for final link inference. Extensive experiments on two real-world data sets demonstrate the superiority of our approach against several competitive baselines for link prediction and sequence matching, validating the contribution of its key components.

Sequential product recommendation, aiming at predicting the products that a target user will interact with soon, has become a hotspot topic. Most of the sequential recommendation models focus on learning from users’ interacted product sequences in a purely data-driven manner. However, they largely overlook the knowledgeable substitutable and complementary relations between products. To address this issue, we propose a novel Substitutable and Complementary Graph-based Sequential Product Recommendation model, namely, SCG-SPRe. The innovations of SCG-SPRe lie in its two main modules: (1) The module of interactive graph neural networks jointly encodes the high-order product correlations in the substitutable graph and the complementary graph into two types of relation-specific product representations. (2) The module of kernel-enhanced transformer networks adaptively fuses multiple temporal kernels to characterize the unique temporal patterns between a candidate product to be recommended and any interacted product in a target behavior sequence. Thanks to the seamless integration of the two modules, SCG-SPRe obtains candidate-dependent user representations for different candidate products to compute the corresponding ranking scores. We conduct extensive experiments on three public datasets, demonstrating SCG-SPRe is superior to competitive sequential recommendation baselines and validating the benefits of explicitly modeling the product-product relations.

Personalized exercise recommendation is a challenging task in the field of artificial intelligence in education due to several problems. First, the mainstream approaches focus more on the exercises that students have not mastered, while overlooking their long-term needs during the learning process. Second, it is difficult to capture students' knowledge states caused by sparse interactions with exercises. Moreover, most recommendation methods are dedicated to the performance of the recommendation w.r.t. accuracy, disregarding the students' learning ability. We introduce a new framework called contrastive personalized exercise recommendation with reinforcement learning (RCL4ER) to tackle these issues. Our framework augments the standard recommendation model with an output layer of self-supervised learning and reinforcement learning. The reinforcement allows the supervised layer to focus on specific rewards, acting as a regularizer. The self-supervised layer provides a powerful signal for parameter updating. Three data augmentation methods are used to provide additional data, which are leveraged to conduct contrastive learning and incorporated into reinforcement learning as supplementary information. In addition, we adopt a trained deep knowledge tracing model to capture the changes in students' knowledge states, so as to establish a dynamic reward mechanism. Experiments of our framework based on four recommendation backbones on several public datasets demonstrate the effectiveness of our RCL4ER, which successfully promotes students' capacity and improves recommendation performance.

The proliferation of location-aware social networks (LSNs) has facilitated the research of user mobility modeling and check-in prediction, thereby benefiting various downstream applications such as precision marketing and urban management. Most of the existing studies only focus on predicting the spatial aspect of check-ins, whereas the joint inference of the spatial and temporal aspects more fits the real application scenarios. Moreover, although social relations have been extensively studied in a recommender system, only a few efforts have been observed in the next check-in location prediction, leaving room for further improvement. In this article, we study the next check-in inference problem, which demands the joint inference of the next check-in location (Where) and time (When) for a target user (Who). We devise a model named ARNPP-GAT, which combines an attention-based recurrent neural point process with a graph attention networks. The core technical insight of ARNPP-GAT is to integrate user long-term representation learning, short-term behavior modeling, and temporal point process into a unified architecture. Specifically, ARNPP-GAT first leverages graph attention networks to learn the long-term representation of users by encoding their social relations. More importantly, the ARNPP endows the model with the capability of characterizing the effects of past check-in events and performing multitask learning to yield the next check-in time and location prediction. Empirical results on two real-world data sets demonstrate that ARNPP-GAT is superior compared with several competitors, validating the contributions of multitask learning and social relation modeling.