Return-conditioned supervised learning (RCSL) in offline reinforcement learning (RL) leverages Transformers to extract behavioral patterns from offline datasets for decision-making. However, it suffers from inherent limitations in comprehensively capturing multi-scale temporal relationships in historical trajectories. Moreover, its return-conditioning mechanism offers limited guidance in exploiting high-quality behavioral patterns, often resulting in suboptimal action generation during inference. To address these challenges, we propose the Advantage Decision ConvMamba (ADCM), a method that integrates multi-scale sequence modeling (MSSM) with advantage policy guidance (APG). ADCM reconstructs historical sequences through patch partitioning and employs Mamba architecture together with causal convolutions to model sparse global dependencies and dense local Markovian dependencies for behavioral pattern discovery. By incorporating relative advantage action sampling based on the Mixture-of-Experts (MoE) framework, ADCM prioritizes high-quality actions during inference, thereby reducing reliance on low-quality behavioral patterns in the dataset. We evaluate ADCM on multiple offline RL benchmarks from D4RL. Experimental results show that ADCM achieves significant improvements over baseline models, with particularly strong performance on suboptimal datasets. The code for ADCM is available at https://github.com/iTom233/ADCM.git.

Knowledge Tracing (KT) aims to model students' knowledge states based on their historical learning sequence, playing a critical role in online education platforms.As the performance of sequence-based KT methods continues to improve, their increasing model complexity and lack of transparency have become significant limitations.In contrast, educational theory-driven KT methods incorporate educationally meaningful features (such as question difficulty or time spent on questions) to enhance interpretability and performance.However, these models typically adopt simpler structures to reduce complexity and avoid overfitting, which limits their ability to effectively capture the sequential characteristics of learning compared to sequence-based methods.To address these limitations, this paper aims to integrate the strengths of both types of methods by proposing an Interpretable KT approach with Difficulty-Aware Attention and Selective State Space Model (ASIKT).Specifically, leveraging educational context, we design a difficulty-enhanced attention mechanism to model students' knowledge retrieval process

Social recommendations leverage social networks to augment the performance of recommender systems. However, the critical task of denoising social information has not been thoroughly investigated in prior research. In this study, we introduce a hierarchical denoising robust social recommendation model to tackle noise at two levels: 1) intra-domain noise, resulting from user multi-faceted social trust relationships, and 2) inter-domain noise, stemming from the entanglement of the latent factors over heterogeneous relations (e.g., user-item interactions, user-user trust relationships). Specifically, our model advances a preference and social psychology-aware methodology for the fine-grained and multi-perspective estimation of tie strength within social networks. This serves as a precursor to an edge weight-guided edge pruning strategy that refines the model's diversity and robustness by dynamically filtering social ties. Additionally, we propose a user interest-aware cross-domain denoising gate, which not only filters noise during the knowledge transfer process but also captures the high-dimensional, nonlinear information prevalent in social domains. We conduct extensive experiments on three real-world datasets to validate the effectiveness of our proposed model against state-of-the-art baselines. We perform empirical studies on synthetic datasets to validate the strong robustness of our proposed model.

Event Argument Extraction (EAE) aims to identify and extract key information such as entities, times, and locations related to specific events from text and serves as a fundamental task for many NLP applications. Recent researches have utilized large language models (LLMs) for EAE, effectively addressing the resource-intensive nature of annotating training datasets for this task. However, when performing EAE on longer texts (document-level EAE), the presence of descriptions unrelated to the events within document-level EAE can lead LLMs to identify incorrect arguments. To address this issue, we propose Simple-Hard Refining: a novel prompt framework that segments EAE into straightforward and complex extraction tasks. Based on the complexity of inference, we divide EAE task into simple-argument extraction and hard-argument extraction. By utilizing a chain of prompt to perform simple and hard argument extraction sequentially, noise introduced by irrelevant description for simple-argument extraction can be effectively alleviated. Furthermore, we explore the potential of LLMs to furnish dependable explanations for their extraction outcomes. We design an explanation-based prompting method that involves a three-step explanation process: relevant sentence extraction, argument role semantic analysis, and argument role entity localization. This method further enhances the extraction accuracy at each stage of the framework. Our experiments demonstrate that our method achieves state-of-the-art performance, surpassing various baselines that utilize LLMs for the EAE task. Ablation studies further verify the effectiveness of each stage of our framework and show the ability of our proposed approach to effectively mitigate noise. Our work contributes to the structured extraction of event argument information using LLMs.

Social networks exhibit a distinct "celebrity effect" whereby influential individuals have a more significant impact on others compared to ordinary individuals, unlike other network structures such as citation networks and knowledge graphs. Despite its common occurrence in social networks, the celebrity effect is frequently overlooked by existing social recommendation methods when modeling social relationships, thereby hindering the full exploitation of social networks to mine similarities between users. In this paper, we fill this gap and propose a Celebrity-aware Graph Contrastive Learning Framework for Social Recommendation (CGCL), which explicitly models the celebrity effect in the social domain. Technically, we measure the different influences of celebrity and ordinary nodes by mining social network structure features, such as closeness centrality. To model the celebrity effect in social networks, we design a novel user-user impact-aware aggregation method, which incorporates the celebrity-aware influence information into the message propagation process. Additionally, we design a graph neural network-based framework which incorporates social semantics into the user-item interaction modeling with contrastive learning-enhanced data augmentation. The experimental results on three real-world datasets show the effectiveness of the proposed framework. We conduct ablation experiments to prove that the key components of our model benefit the recommendation performance improvement.

User preference profile is generally significant to marketing strategy decisions as well as user experience improvement for mobile telecom operators. To establish preference profile, perators create a hierarchical taxonomy of preference and classify records of user browsing history on mobile internet by the taxonomy to measure user preference. However, the incompleteness of recorded browsing history makes it nontrivial to observe all the users' preferences. To complete missing preferences, recommendation based methodology is commonly exploited. Although taxonomy contains the semantic relationships between preferences, there are merely a few works that explored them for recommendation. We extend these works by clearly defining the relation types and learning relation strengths among preferences in the taxonomy, on which we propose a Taxonomy-induced Matrix Factorization (TMF) model. We perform experiments on a large dataset of user browsing data from a Chinese telecom operator. The results show that our proposed model outperforms the standard matrix factorization model. In addition, the relations learned by TMF are detailed analyzed to show their inherent effects for the inference improvement.

Churn prediction, which is to identify who are prone to abandon the subscription, is of high significance for the operators to retain the potential churners. It should be noted that, in practice, the earlier the churners are identified, the more effective strategies the operators can develop to retain them. While the earlier prediction of customer churn in telecommunication industry has not been well investigated and the predicting accuracy of previous work degrades unacceptably when the interval between observed attributes and predicted labels is prolonged. In this paper, from a different perspective, we study the effectiveness to find the churners as early as possible with the accuracy being high enough, which we define as Early Churn Prediction. The predictive performance of the proposed model, which takes time series attributes and influence of churning contacts in social network into consideration, is investigated. We evaluate the method using a 12-month-long dataset collected by one of the largest operators in China. The results show that our model significantly outperforms the previous work especially when the prediction interval is larger than 3 months.