Transformer-based sequential recommendation (SR) models excel at modeling long-range dependencies, but suffer from high computational costs and catastrophic forgetting during continuous updates. Although continual learning has been applied to recommendation, existing methods gradually forget long-term user preferences and remain underexplored in SR. In this paper, we introduce Continual Sequential Transformer for Recommendation (CSTRec), which effectively adapt to current interests by leveraging preserved historical knowledge. Its core is Continual Sequential Attention (CSA), a linear attention tailored for continual SR, which partially retain historical knowledge without direct access to prior data. CSA features: (1) Cauchy-Schwarz Normalization to stabilize learning over time under uneven user interaction frequencies, and (2) Collaborative Interest Enrichment via shared, learnable interest pools to mitigate forgetting. We also introduce a new technique for new user adaptation by transferring historical knowledge from existing users with similar interests. Extensive experiments show CSTRec's superior performance in both knowledge retention and acquisition. Our code is available at https://github.com/Gyu-Seok0/CSTRec_WSDM26.

Modern recommender systems operate in uniquely dynamic settings: user interests, item pools, and popularity trends shift continuously, and models must adapt in real time without forgetting past preferences. While existing tutorials on continual or lifelong learning cover broad machine learning domains (e.g., vision and graphs), they do not address recommendation-specific demands-such as balancing stability and plasticity per user, handling cold-start items, and optimizing recommendation metrics under streaming feedback. This tutorial aims to make a timely contribution by filling that gap. We begin by reviewing the background and problem settings, followed by a comprehensive overview of existing approaches. We then highlight recent efforts to apply continual learning to practical deployment environments, such as resource-constrained systems and sequential interaction settings. Finally, we discuss open challenges and future research directions. We expect this tutorial to benefit researchers and practitioners in recommender systems, data mining, AI, and information retrieval across academia and industry.

Many modern recommender systems represent user and item attributes as embedding vectors, relying on them for accurate recommendations. However, entangled embeddings often capture not only intrinsic property factors (e.g., user interest in item property) but also popularity factors (e.g., user conformity to item popularity) indistinguishably. These embeddings, influenced by popularity distribution, may face challenges when the popularity distribution at test time differs from historical distribution. Existing remedies in the literature involve disentangled embedding learning, which aims to separately capture intrinsic and popularity factors, demonstrating plausible generalization during popularity distribution shifts. However, we highlight that these methods often overlook a crucial aspect of popularity shifts-their temporal nature-in both training and inference phases. To address this, we propose Temporal Popularity distribution shift generalizABle recommender system (TPAB), a novel disentanglement framework incorporating temporal popularity. TPAB introduce a new (1) temporal-aware embedding design for users and items. Within this design, (2) popularity coarsening and (3) popularity bootstrapping are proposed to enhance generalization further. We also provide theoretical analysis showing that the bootstrapping loss eliminates the effect of popularity on the learned model. During inference, we infer test-time popularity and corresponding embeddings, using them alongside property embeddings for prediction. Extensive experiments on real-world datasets validate TPAB, showcasing its outstanding generalization ability during temporal popularity distribution shifts.

In the era of big data and AI, recommender systems must adapt to evolving user preferences and new users/items to maintain high-quality recommendations. Fine-tuning, which updates model parameters using only new data, offers an efficient alternative to full retraining but struggles to balance stability (retaining past knowledge) and plasticity (adapting to new knowledge). While existing methods prioritize stability to address catastrophic forgetting, we argue that plasticity must also be explicitly strengthened, especially for users with rapidly changing preferences. In this work, we propose PlastIcity and StAbility balancing continual recommender systems (PISA), a novel framework that adaptively balances stability and plasticity based on user preference shifts. PISA quantifies preference shifts as changes in user distances to item clusters, and then guides user embeddings by prioritizing stability for stable users and plasticity for dynamic users. To achieve this, PISA leverages backward knowledge from the previous model and forward knowledge from fine-tuning on current data. During training, PISA maximizes mutual information between user-specific parameters and the relevant reference knowledge. Theoretically, we show that enhancing plasticity mitigates distribution shifts more effectively than fine-tuning alone. Empirically, extensive experiments on three real-world datasets validate PISA's superiority over existing methods and highlight the contributions of its components.

Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models. Most existing studies are rooted in a class-rebalancing (CR) perspective and address class imbalance with class-wise reweighting or resampling. In this work, we approach the root cause of class-imbalance bias from an topological paradigm. Specifically, we theoretically reveal two **fundamental phenomena in the graph topology** that greatly exacerbate the predictive bias stemming from class imbalance. On this basis, we devise a lightweight topological augmentation framework BAT to mitigate the class-imbalance bias without class rebalancing. Being orthogonal to CR, BAT can function as an **efficient plug-and-play module** that can be seamlessly combined with and significantly boost existing CR techniques. Systematic experiments on real-world imbalanced graph learning tasks show that BAT can deliver up to 46.27% performance gain and up to 72.74% bias reduction over existing techniques. Code, examples, and documentations are available at https://github.com/ZhiningLiu1998/BAT.

User-side group fairness is crucial for modern recommender systems, alleviating performance disparities among user groups defined by sensitive attributes like gender, race, or age. In the everevolving landscape of user-item interactions, continual adaptation to newly collected data is crucial for recommender systems to stay aligned with the latest user preferences. However, we observe that such continual adaptation often worsen performance disparities. This necessitates a thorough investigation into user-side fairness in dynamic recommender systems. This problem is challenging due to distribution shifts, frequent model updates, and nondifferentiability of ranking metrics. To our knowledge, this paper presents the first principled study on ensuring user-side fairness in dynamic recommender systems. We start with theoretical analyses on fine-tuning v.s. retraining, showing that the best practice is incremental fine-tuning with restart. Guided by our theoretical analyses, we propose FAir Dynamic rEcommender (FADE), an end-to-end fine-tuning framework to dynamically ensure user-side fairness over time. To overcome the non-differentiability of recommendation metrics in the fairness loss, we further introduce Differentiable Hit (DH) as an improvement over the recent NeuralNDCG method, not only alleviating its gradient vanishing issue but also achieving higher efficiency. Besides that, we also address the instability issue of the fairness loss by leveraging the competing nature between the recommendation loss and the fairness loss. Through extensive experiments on real-world datasets, we demonstrate that FADE effectively and efficiently reduces performance disparities with little sacrifice in the overall recommendation performance.

The goal of directed network embedding is to represent the nodes in a given directed network as embeddings that preserve the asymmetric relationships between nodes. While a number of directed network embedding methods have been proposed, we empirically show that the existing methods lack out-of-distribution generalization abilities against degree-related distributional shifts. To mitigate this problem, we propose ODIN (Out-of-Distribution Generalized Directed Network Embedding), a new directed NE method where we model multiple factors in the formation of directed edges. Then, for each node, ODIN learns multiple embeddings, each of which preserves its corresponding factor, by disentangling interest factors and biases related to in- and out-degrees of nodes. Our experiments on four real-world directed networks demonstrate that disentangling multiple factors enables ODIN to yield out-of-distribution generalized embeddings that are consistently effective under various degrees of shifts in degree distributions. Specifically, ODIN universally outperforms 9 state-of-the-art competitors in 2 LP tasks on 4 real-world datasets under both identical distribution (ID) and non-ID settings. The code is available at https://github.com/hsyoo32/odin.

The directed network embedding problem is to represent the nodes in a given directed network as embeddings (i.e., low-dimensional vectors) that preserve the asymmetric relationships between nodes. While a number of approaches have been developed for this problem, we point out that existing approaches commonly face difficulties in accurately preserving asymmetric proximities between nodes in a sparse network containing a large number of low out- and in-degree nodes. In this paper, we focus on addressing this intrinsic difficulty caused by the lack of information. We first introduce the concept of virtual negative edges (VNEs), which represent latent negative relationships between nodes. Based on the concept, we propose a novel DIrected NE approach with VIrtual Negative Edges, named as DIVINE. DIVINE carefully decides the number and locations of VNEs to be added to the input network. Once VNEs are added, DIVINE learns embeddings by exploiting both the signs and directions of edges. Our experiments on four real-world directed networks demonstrate that adding VNEs alleviates the lack of information about low-degree nodes, thereby enabling DIVINE to yield high-quality embeddings that accurately capture asymmetric proximities between nodes. Specifically, the embeddings obtained by DIVINE lead to up to 10.16% more accurate link prediction, compared to those obtained by state-of-the-art competitors.

Testing of mobile applications becomes more importantas the usage of smartphones increases. Many studiesuse Monkey, a basic testing tool provided by Android. WithMonkey, however, it is hard to replay the behavior of actualusers. In this work, we present a mobile app testing methodthat automatically records and replays user event logs on thesmartphone. From replay testing experiments, we have shownthat our method generates 2.3 times more valid input events and1.4 times faster than fuzz testing.