Memory behavior modeling aims to predict individual performance over time and uncover underlying cognitive mechanisms. However, existing approaches often struggle to balance predictive accuracy, domain generalization, and model interpretability. To address this, we propose ROKAN, a cognitively inspired and symbolically interpretable memory modeling framework. Based on the Multiscale Context Model, ROKAN formalizes the evolution of memory traces as a differentiable Ordinary Differential Equation system, implemented via Kolmogorov-Arnold Networks to derive human-readable symbolic expressions. To enhance generalization across heterogeneous learning domains, we design an Adaptive Domain-Aware loss function, which integrates Empirical Risk Minimization with Distributionally Robust Optimization through dynamic domain-aware weighting. Our experiments demonstrate that ROKAN significantly outperforms existing mainstream methods in both predictive accuracy and domain generalization. The symbolic expressions were found to exhibit formal consistency with classical memory theories, which lends support to the model's theoretical assumptions and empirical performance, and provides a new pathway toward theoretically grounded white-box memory modeling. Our code is available at https://github.com/hellowads/ROKAN.

Memory behavior modeling is a key topic in cognitive psychology and education. Traditional approaches use experimental data to build memory equations, but these models often lack precision and are debated in form. Recently, data-driven methods have improved predictive accuracy but struggle with interpretability, limiting cognitive insights. Although knowledge-informed neural networks have succeeded in fields like physics, their use in behavior modeling is still limited. This paper proposes a Self-evolving Psychology-informed Neural Network (SPsyINN), which leverages classical memory equations as knowledge modules to constrain neural network training. To address challenges such as the difficulty in quantifying descriptors and the limited interpretability of classical memory equations, a genetic symbolic regression algorithm is introduced to conduct evolutionary searches for more optimal expressions based on classical memory equations, enabling the mutual progress of the knowledge module and the neural network module. Specifically, the proposed approach combines genetic symbolic regression and neural networks in a parallel training framework, with a dynamic joint optimization loss function ensuring effective knowledge alignment between the two modules. Then, for addressing the training efficiency differences arising from the distinct optimization methods and computational hardware requirements of genetic algorithms and neural networks, an asynchronous interaction mechanism mediated by proxy data is developed to facilitate effective communication between modules and improve optimization efficiency. Finally, a denoising module is integrated into the neural network to enhance robustness against data noise and improve generalization performance. Experimental results on five large-scale real-world memory behavior demonstrate that SPsyINN outperforms state-of-the-art methods in predictive accuracy. Ablation studies confirm the model's co-evolution capability, improving accuracy while discovering more interpretable memory equations, showing its potential for psychological research. Our code is released at: https://github.com/JiaqiDijon/SPsyINN

Incorporating a critiquing component into recommender applications facilitates the enhancement of user perception. Typically, critique-able recommender systems adapt the model parameters and update the recommendation list in real-time through the analysis of user critiquing keyphrases in the inference phase. The current critiquing methods necessitate the designation of a dedicated recommendation model to estimate user relevance to the critiquing keyphrase during the training phase preceding the recommendations update. This paradigm restricts the applicable scenarios and reduces the potential for keyphrase exploitation. Furthermore, these approaches ignore the issue of catastrophic forgetting caused by continuous modification of model parameters in multi-step critiquing. Thus, we present a general Representative Items Sampling Framework for Critiquing on Knowledge Graph Recommendation (RISC) implemented as a plug-in, which offers a new paradigm for critiquing in mainstream recommendation scenarios. RISC leverages the knowledge graph to sample important representative items as a hinge to expand and convey information from user critiquing, indirectly estimating the relevance of the user to the critiquing keyphrase. Consequently, the necessity for specialized user-keyphrase correlation modules is eliminated with respect to a variety of knowledge graph recommendation models. Moreover, we propose a Weight Experience Replay (WER) approach based on KG to mitigate catastrophic forgetting by reinforcing the user's prior preferences during the inference phase. Our extensive experimental findings on three real-world datasets and three knowledge graph recommendation methods illustrate that RISC with WER can be effectively integrated into knowledge graph recommendation models to efficiently utilize user critiquing for refining recommendations and mitigate catastrophic forgetting.

Knowledge Tracing (KT) is the task of predicting students’ future performance based on their past interactions with educational resources. A key aspect of KT is representation learning, which aims to capture meaningful features from students’ learning behaviors to improve prediction performance. Recently, contrastive learning methods have shown great promise in representation learning. As a result, KT models based on contrastive learning have been introduced to enhance representation learning for KT. However, these models have posed several challenges. Firstly, most of these models adopt the contrastive learning approach used in other fields, which involves data augmentation followed by contrastive learning, yet effectively applying data augmentation in KT remains an open challenge. Secondly, these models typically apply contrastive learning to only one of the fundamental components of KT: questions, interactions, or knowledge states, thereby limiting their overall performance. To address these issues, this article proposes a Multi-level Contrastive learning model for Knowledge Tracing (MCKT). MCKT (The code can be found at https://github.com/lilstrawberry/MCKT .) does not rely on data augmentation strategies; instead, it deeply integrates domain knowledge and performs contrastive learning at three levels: questions, interactions, and knowledge states. Experimental results on four publicly available datasets, compared against a total of 20 state-of-the-art KT models, demonstrate that MCKT consistently outperforms other models. Subsequent experiments further validate the effectiveness of the multi-level contrastive learning approach.

Knowledge Tracing (KT) aims to predict students’ future performance on answering questions based on their historical exercise sequences. To alleviate the problem of data sparsity in KT, recent works have introduced auxiliary information to mine question similarity, resulting in the enhancement of question embeddings. Nonetheless, there remains a gap in developing an approach that effectively incorporates various forms of auxiliary information, including relational information (e.g., question–student , question–skill relation), relationship attributes (e.g., correctness indicating a student's performance on a question), and node attributes (e.g., student ability ). To tackle this challenge, the Similarity-enhanced Question Embedding (SimQE) method for KT is proposed, with its central feature being the utilization of weighted and attributed meta-paths for extracting question similarity. To capture multi-dimensional question similarity semantics by integrating multiple relations, various meta-paths are constructed for learning question embeddings separately. These embeddings, each encoding different similarity semantics, are then fused to serve the task of KT. To capture finer-grained similarity by leveraging the relationship attributes and node attributes on the meta-paths, the biased random walk algorithm is designed. In addition, the auxiliary node generation method is proposed to capture high-order question similarity. Finally, extensive experiments conducted on six datasets demonstrate that SimQE performs the best among 10 representative question embedding methods. Furthermore, SimQE proves to be more effective in alleviating the problem of data sparsity.

Knowledge Tracing (KT) is vital for education, continuously monitoring students' knowledge states (mastery of knowledge) as they interact with online education materials. Despite significant advancements in deep learning-based KT models, existing approaches often struggle to strike the right balance in granularity, leading to either overly coarse or excessively fine tracing and representation of students' knowledge states, thereby limiting their performance. Additionally, achieving a high-performing model while ensuring interpretability presents a challenge. Therefore, in this paper, we propose a novel approach called Multiscale-state-based Interpretable Knowledge Tracing (MIKT). Specifically, MIKT traces students' knowledge states on two scales: a coarse-grained representation to trace students' domain knowledge state, and a fine-grained representation to monitor their conceptual knowledge state. Furthermore, the classical psychological measurement model, IRT (Item Response Theory), is introduced to explain the prediction process of MIKT, enhancing its interpretability without sacrificing performance. Additionally, we extended the Rasch representation method to effectively handle scenarios where questions are associated with multiple concepts, making it more applicable to real-world situations. We extensively compared MIKT with 20 state-of-the-art KT models on four widely-used public datasets. Experimental results demonstrate that MIKT outperforms other models while maintaining its interpretability. Moreover, experimental observations have revealed that our proposed extended Rasch representation method not only benefits MIKT but also significantly improves the performance of other KT baseline models. The code can be found at https://github.com/lilstrawberry/MIKT.

Graph embedding (GE) aims to acquire low-dimensional node representations while maintaining the graph’s structural and semantic attributes. Intelligent tutoring systems (ITS) signify a noteworthy achievement in the fusion of AI and education. Utilizing GE to model ITS can elevate their performance in predictive and annotation tasks. Current GE techniques, whether applied to heterogeneous or dynamic graphs, struggle to efficiently model ITS data. The GEs within ITS should retain their semidynamic, independent, and smooth characteristics. This article introduces a heterogeneous evolution network (HEN) for illustrating entities and relations within an ITS. Additionally, we introduce a temporal extension graph neural network (TEGNN) to model both evolving and static nodes within the HEN. In the TEGNN framework, dynamic nodes are initially improved over time through temporal extension (TE), providing an accurate depiction of each learner’s implicit state at each time step. Subsequently, we propose a stochastic temporal pooling (STP) strategy to estimate the embedding sets of all evolving nodes. This effectively enhances model efficiency and usability. Following this, a heterogeneous aggregation network is devised to proficiently extract heterogeneous features from the HEN. This network employs both node-level and relation-level attention mechanisms to craft aggregated node features. To emphasize the superiority of TEGNN, we perform experiments on several real ITS datasets and show that our method significantly outperforms the state-of-the-art approaches. The experiments validate that TE serves as an efficient framework for modeling temporal information in GE, and STP not only accelerates the training process but also enhances the resultant accuracy.

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.