Sequential Recommender Systems (SRSs) predict items of interest for users based on their historical interactions. Two key popular paradigms for SRs are unidirectional Next Item Prediction (NIP) and bidirectional Masked Language Modeling (MLM). NIP performs well in recommendation tasks but is constrained by its reliance on prior information and a rigid temporal order assumption, limiting its ability to capture dynamic personalized preferences. MLM, on the other hand, enhances sequence representation and personalization by leveraging bidirectional context, but its objective is misaligned with recommendation tasks. To this end, we develop MetaSR, a meta-learning-based SR approach that ingeniously combines both paradigms into a unified framework to enhance personalized sequential recommendation. Specifically, we treat the mask prediction task in MLM as the inner-loop task (i.e., the support set), adjusting mask items dynamically to generate sequences that match individual behavioral patterns to capture personalized user preferences. In the outer loop (i.e., the query set), we adopt the NIP paradigm to directly train MetaSR toward the SR target. The joint training bridges the gap between MLM and SRs tasks, also overcoming NIP’s limitations in capturing dynamic personalized preferences. Furthermore, to improve the overall efficiency, we develop a reinforcement learning-based Adaptive Masked Sequence Selection (AMSS) mechanism, automatically selecting the optimal masking prediction tasks within the meta-learning support set to accelerate the meta-learning and achieve better personalization. Experiments on multiple public benchmark datasets show that MetaSR significantly outperforms existing SRs models. The dataset and codes are available at MetaSR .

Knowledge Tracing (KT) is a fundamental task in personalized education, aiming to predict student performance by modeling their evolving concept mastery. Recent state-of-the-art approaches adopt multi-graph architectures to capture diverse concept and behavior relations. However, such models often suffer from graph imbalance, where one graph branch dominates training, undermining the benefits of structural integration. To address this, we propose AMBER (Adaptive Meta-Balanced Ensemble Representation learning), a KT framework designed to promote balanced learning across heterogeneous graph structures. AMBER introduces an external dual-graph teacher to guide the learning of ensemble representations. As the teacher itself may encode graph imbalance bias, we further incorporate a meta-distillation strategy that adaptively adjusts the teacher using student feedback, amplifying signals beneficial to underperforming branches. In addition, an adaptive graph rebalancing strategy is introduced to balance the optimization of different graph branches in real time, preventing dominance by any single structure. Experiments on three real-world datasets show that AMBER consistently outperforms competitive baselines. By promoting balanced optimization across graphs, AMBER enables more effective integration of heterogeneous learning signals in KT, providing a robust and scalable solution for personalized education. Code is available at https://github.com/AMBER2025KT/AMBER2025CIKM.

The increasingly popular usage of wearable devices provides users with the ability to continuously track their health conditions or physical activities. Such system is however vulnerable to user spoofing, in which a user distributes his/her device to other users such that the data collected from these users could be claimed to be his/her own. Thus, it is critical to identify the user for many wearable devices, allowing the sensing data to be labeled properly. In this paper, we propose a user identification system by leveraging the users whispered sound to mitigate user spoofing for wearable devices. Our system exploits the contact microphone placed into contact with the body to capture the users whispered sound for user identification. Given the captured acoustic data, our system first identifies frames which contain whispered events. Our system then calculates acoustic features from the identified whispered frames to determine whether the voice is collected when the microphone is on the body. Moreover, to make our system robust, we assign different quality weights to the whispers phonemes by considering their consistency (i.e., intra users differences) and distinctiveness (i.e., inter users differences) simultaneously. Our experiments demonstrate that our system is robust and accurate across various scenarios.