Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

Recent advancements in generative models have positioned them as one of the principal tools for sequential recommendation due to their exceptional sample diversity and generalization capabilities. Among these, diffusion model-based sequential recommenders have achieved remarkable success. However, most existing approaches still face critical challenges, resulting in suboptimal generation quality: (1) They fail to leverage multimodal knowledge for constructing item representations with well-structured distributional characteristics and semantically enriched information; (2) They predominantly rely on discrete diffusion processes, leading to high error accumulation, reduced time efficiency, and constrained controllability in generative sampling. To mitigate these challenges, we propose LSGM4Rec, a novel framework that integrates Large Language Models (LLMs) with advanced multimodal encoding models to establish multimodal fusion embeddings for items. This design ensures distinct distributional characteristics while enabling the incorporation of semantically rich modal features into guidance condition. Furthermore, we pioneer the stochastic differential equations (SDEs) for recommendation, facilitating smooth transitions between data distributions and enabling optimal trade-off between sampling efficiency and generation quality. Extensive experiments on three datasets demonstrate that LSGM4Rec outperforms existing state-of-the-art sequential recommendation methods.

Sequential recommendation is a popular task in academic research and close to real-world application scenarios, where the goal is to predict the next action(s) of the user based on his/her previous sequence of actions. In the training process of recommender systems, the loss function plays an essential role in guiding the optimization of recommendation models to generate accurate suggestions for users. However, most existing sequential recommendation techniques focus on designing algorithms or neural network architectures, and few efforts have been made to tailor loss functions that fit naturally into the practical application scenario of sequential recommender systems. Ranking-based losses, such as cross-entropy and Bayesian Personalized Ranking (BPR) are widely used in the sequential recommendation area. We argue that such objective functions suffer from two inherent drawbacks: i) the dependencies among elements of a sequence are overlooked in these loss formulations; ii) instead of balancing accuracy (quality) and diversity, only generating accurate results has been over emphasized. We therefore propose two new loss functions based on the Determinantal Point Process (DPP) likelihood, that can be adaptively applied to estimate the subsequent item or items. The DPP-distributed item set captures natural dependencies among temporal actions, and a quality vs. diversity decomposition of the DPP kernel pushes us to go beyond accuracy-oriented loss functions. Experimental results using the proposed loss functions on three real-world datasets show marked improvements over state-of-the-art sequential recommendation methods in both quality and diversity metrics.

The sequential recommendation selects and recommends next items for users by modeling their historical interaction sequences, where the chronological order of interactions plays an important role. Most sequential recommendation methods only pay attention to the order information among the interactions and ignore the time intervals information, which leads to the limitations of capturing dynamic user interests. And previous work neglects diversity in order to improve recommendation accuracy. The model Temporal Self-Attention and Multi-Preference Learning (TSAMPL) is proposed to improve sequential recommendation, which learns dynamic and general user interests separately. The proposed temporal gate self-attention network is introduced to learn dynamic user interests, which takes both contextual information and temporal dynamics into account. To model general user interests, we employ a multi-preference matrix to learn users’ multiple types of preferences for improving recommendation diversity. Finally, the interest fusion module combines dynamic user interests (accuracy) and general user interests (diversity) adaptively. The experiments in sequential recommendation confirm our method is superior to all comparison methods, we also study the impact of each component in the model.

Sequential recommendation aims to capture users' dynamic interest and predicts the next item of users' preference. Most sequential recommendation methods use a deep neural network as sequence encoder to generate user and item representations. Existing works mainly center upon designing a stronger sequence encoder. However, few attempts have been made with training an ensemble of networks as sequence encoders, which is more powerful than a single network because an ensemble of parallel networks can yield diverse prediction results and hence better accuracy. In this paper, we present Ensemble Modeling with contrastive Knowledge Distillation for sequential recommendation (EMKD). Our framework adopts multiple parallel networks as an ensemble of sequence encoders and recommends items based on the output distributions of all these networks. To facilitate knowledge transfer between parallel networks, we propose a novel contrastive knowledge distillation approach, which performs knowledge transfer from the representation level via Intra-network Contrastive Learning (ICL) and Cross-network Contrastive Learning (CCL), as well as Knowledge Distillation (KD) from the logits level via minimizing the Kullback-Leibler divergence between the output distributions of the teacher network and the student network. To leverage contextual information, we train the primary masked item prediction task alongside the auxiliary attribute prediction task as a multi-task learning scheme. Extensive experiments on public benchmark datasets show that EMKD achieves a significant improvement compared with the state-of-the-art methods. Besides, we demonstrate that our ensemble method is a generalized approach that can also improve the performance of other sequential recommenders. Our code is available at this link: https://github.com/hw-du/EMKD.

Recent deep sequential recommendation models often struggle to effectively model key characteristics of user behaviors, particularly in handling sequence length variations and capturing diverse interaction patterns. We propose STAR-Rec, a novel architecture that synergistically combines preference-aware attention and state-space modeling through a sequence-level mixture-of-experts framework. STAR-Rec addresses these challenges by: (1) employing preference-aware attention to capture both inherently similar item relationships and diverse preferences (2) utilizing state-space modeling to efficiently process variable-length sequences with linear complexity, and (3) incorporating a mixture-of-experts component that adaptively routes different behavioral patterns to specialized experts, handling both focused category-specific browsing and diverse category exploration patterns. We theoretically demonstrate how the state space model and attention mechanisms can be naturally unified in recommendation scenarios, where SSM captures temporal dynamics through state compression while attention models both similar and diverse item relationships. Extensive experiments on four real-world datasets demonstrate that STAR-Rec consistently outperforms state-of-the-art sequential recommendation methods, particularly in scenarios involving diverse user behaviors and varying sequence lengths. The implementation code is available anonymously online for easy reproducibility.

Sequential recommendations aim to capture users’ preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user’s historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations.

When long-term user proles are not available, session-based recommendation methods are used to predict the user's next actions from anonymous sessions-based data. Recent advances in session-based recommendation highlight the necessity of modeling not only user sequential behaviors but also the user's main interest in a session, while avoiding the eect of unintended clicks causing interest drift of the user. In this work, we propose a Dual Transformer Encoder Recommendation model (DTER) as a solution to address this requirement. The idea is to combine the following recipes: (1) a Transformer-based model with dual encoders capable of modeling both sequential patterns and the main interest of the user in a session; (2) a new recommendation model that is designed for learning richer session contexts by conditioning on all permutations of the session prex. This approach provides a unied framework for leveraging the ability of the Transformer's self-attention mechanism in modeling session sequences while taking into account the user's main interest in the session. We empirically evaluate the proposed method on two benchmark datasets. The results show that DTER outperforms state-of-the-art session-based recommendation methods on common evaluation metrics.

Group-aware graph neural networks for sequential recommendation

Sequential recommender systems, as a specialized branch of recommender systems that can capture users’ dynamic preferences for more accurate and timely recommendations, have made significant progress. Recently, due to increasing concerns about user data privacy, some researchers have implemented federated learning for sequential recommendation, a.k.a., Federated Sequential Recommender Systems (FedSeqRecs), in which a public sequential recommender model is shared and frequently transmitted between a central server and clients to achieve collaborative learning. Although these solutions mitigate user privacy to some extent, they present two significant limitations that affect their practical usability: (1) They require a globally shared sequential recommendation model. However, in real-world scenarios, the recommendation model constitutes a critical intellectual property for platform and service providers. Therefore, service providers may be reluctant to disclose their meticulously developed models. (2) The communication costs are high as they correlate with the number of model parameters. This becomes particularly problematic as the current FedSeqRec will be inapplicable when sequential recommendation marches into a large language model era. To overcome the above challenges, this article proposes a parameter transmission-free federated sequential recommendation framework (PTF-FSR), which ensures both model and data privacy protection to meet the privacy needs of service providers and system users alike. Furthermore, since PTF-FSR only transmits prediction results under privacy protection, which are independent of model sizes, this new federated learning architecture can accommodate more complex and larger sequential recommendation models. Extensive experiments conducted on three widely used recommendation datasets, employing various sequential recommendation models from both ID-based and ID-free paradigms, demonstrate the effectiveness and generalization capability of our proposed framework. To facilitate future research in this direction, we release our code at https://github.com/hi-weiyuan/PTF-FSR .

The convergence of machine learning (ML) and applied neuroscience continues to accelerate, driven by the synergistic demands of intelligent systems and deepening insights into the human nervous system. Building upon the success of Machine Learning and Applied Neuroscience: Volume I [https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/ fnbot.2023.1191045/full], this second volume brings together cutting-edge research that exemplifies how computational intelligence-particularly deep learning, selfsupervision, and generative modeling-can address complex challenges in neurorobotics, neurorehabilitation, and behavior-aware intelligent systems.The four contributions in this Research Topic span a compelling spectrum: from the diagnosis of gait dysfunction in stroke survivors using cost-sensitive classifiers, to the generation of lifelike 3D human motion through generative adversarial networks (GANs), to next-generation sequential recommendation systems that model multigranularity behavior and feature interactions. Though seemingly diverse, these works share a unifying vision: leveraging advanced ML not only to model neural or behavioral data more accurately, but to extract clinically or functionally meaningful signals that empower real-world applications.One axis of innovation lies in clinical decision support through interpretable and robust ML. In their study, "Machine learning-based gait adaptation dysfunction identification using CMill-based gait data", Yang et al. (2024) (https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/ fnbot.2024.1421401/full) tackle gait adaptation dysfunction (GAD)-a pervasive yet under-assessed impairment in post-stroke patients. Using data from an augmentedreality CMill treadmill, they extract kinematic and adaptability features across four ecologically valid tasks (e.g., obstacle avoidance, slalom walking). Among five classifiers evaluated, the AdaCost algorithm-designed to handle class imbalance and misclassification costs-achieved the best sensitivity (80%) and AUC (0.75). Crucially, feature importance analysis revealed that obstacle avoidance success and gait speed were the top predictors, aligning with clinical intuition and offering actionable biomarkers for rehabilitation planning. This work demonstrates how thoughtful integration of domain-aware data collection and cost-sensitive learning can yield deployable diagnostic aids.Parallel advances emerge in synthetic data generation for human-motion understanding. Wang et al. (2024), entitled "3D human pose data augmentation using Generative Adversarial Networks for robotic-assisted movement quality assessment" (https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/ fnbot.2024.1371385/full) introduce a novel GANs-SVM-DenseNet pipeline to augment 3D human pose datasets-addressing a persistent bottleneck in training data scarcity and limited motion diversity. Their framework uses robotic-assisted capture for highfidelity grounding, GANs to generate realistic and varied motion sequences, DenseNet for hierarchical feature extraction, and SVM for precise motion-quality classification. Evaluated across four benchmarks (Human3.6M, MPI-INF-3DHP, NTU RGB+D, HumanEva), the model outperforms state-of-the-art methods in both accuracy (>96% on Human3.6M) and efficiency (30% faster inference). By closing the loop between data synthesis, feature learning, and quality assessment, this approach paves the way for scalable, robot-in-the-loop systems in sports science, rehabilitation, and virtual reality.Complementing these human-centered applications, two articles push the frontiers of sequential modeling in behavior-aware AI, with implications for neuroscience-inspired user modeling. Zhu et al. (2024a), at "Multi-granularity contrastive learning model for next POI recommendation" (https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/ fnbot.2024.1428785/full), propose MGCL (Multi-Granularity Contrastive Learning) for next Point-of-Interest (POI) recommendation. Recognizing that user mobility is expressed not only at the location level but also through regions and categories, MGCL constructs multi-granular sequences and applies contrastive learning to encourage mutual enhancement across granularities. Experiments on three real-world datasets show consistent gains over 11 baselines-validating that modeling collaborative signals across abstraction levels mitigates data sparsity and enriches preference representation. This principle resonates with hierarchical processing in the brain, where sensory inputs are integrated across spatial and categorical scales.Extending this theme, the same team presents FIDS (Feature Interaction Dual Self-Attention Network) in the article entitled "Feature Interaction Dual Self-attention network for sequential recommendation" (https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/ fnbot.2024.1456192/full), which challenges the assumption that item features are independent. FIDS first uses intra-item self-attention to model higher-order feature interactions (e.g., between brand, category, and seller in e-commerce), then employs dual self-attention streams to capture sequential dependencies both in item sequences and in the derived integrated-feature sequences. This architecture outperforms strong baselines-including FDSA and SASRec-by up to 9% in HR@5 on the Tmall dataset, proving that explicit modeling of feature interdependence enhances behavioral prediction. Such architectures may inform computational models of cognitive binding, where disparate perceptual attributes are fused into coherent percepts.Collectively, these works illustrate a maturing field where ML and neuroscience coevolve: ML architectures grow more neurobiologically plausible (e.g., through hierarchy, attention, and contrastive learning), while neuroscience and clinical applications benefit from increasingly nuanced, robust, and interpretable models.Looking ahead, key challenges remain-particularly around model explainability, realtime deployment in assistive robotics, cross-population generalizability, and ethical handling of behavioral data. Yet the trajectory is clear: the fusion of machine intelligence and applied neuroscience will continue to yield technologies that are not only more intelligent but also more human-centered.

In recommender systems, modeling user-item behaviors is essential for user representation learning. Existing sequential recommenders consider the sequential correlations between historically interacted items for capturing users' historical preferences. However, since users' preferences are by nature time-evolving and diversified, solely modeling the historical preference (without being aware of the time-evolving trends of preferences) can be inferior for recommending complementary or fresh items and thus hurt the effectiveness of recommender systems. In this paper, we bridge the gap between the past preference and potential future preference by proposing the future-aware diverse trends (FAT) framework. By future-aware, for each inspected user, we construct the future sequences from other similar users, which comprise of behaviors that happen after the last behavior of the inspected user, based on a proposed neighbor behavior extractor. By diverse trends, supposing the future preferences can be diversified, we propose the diverse trends extractor and the time-aware mechanism to represent the possible trends of preferences for a given user with multiple vectors. We leverage both the representations of historical preference and possible future trends to obtain the final recommendation. The quantitative and qualitative results from relatively extensive experiments on real-world datasets demonstrate the proposed framework not only outperforms the state-of-the-art sequential recommendation methods across various metrics, but also makes complementary and fresh recommendations.

Recommending a limited number of Point-of-Interests (POIs) a user will visit next has become increasingly important to both users and POI holders for Location-Based Social Networks (LBSNs). However, POI recommendation is a challenging task since complex sequential patterns and rich contexts are contained in extremely sparse user check-in data. Recent studies show that embedding techniques effectively incorporate POI contextual information to alleviate the data sparsity issue, and Recurrent Neural Network (RNN) has been successfully employed for sequential prediction. Nevertheless, existing POI recommendation approaches are still limited in capturing user personalized preference due to separate embedding learning or network modeling. To this end, we propose a novel unified spatio-temporal neural network framework, named PPR, which leverages users’ check-in records and social ties to recommend personalized POIs for querying users by joint embedding and sequential modeling. Specifically, PPR first learns user and POI representations by joint modeling User-POI relation, sequential patterns, geographical influence, and social ties in a heterogeneous graph and then models user personalized sequential patterns using the designed spatio-temporal neural network based on LSTM model for the personalized POI recommendation. Furthermore, we extend PPR to an end-to-end recommendation model by jointly learning node representations and modeling user personalized sequential preference. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms state-of-the-art baselines for successive POI recommendation in terms of Accuracy, Precision, Recall and NDCG. The source code is available at: https://www.anonymous.4open.science/r/DSE-1BEC.

Sequential recommendation (SR) models based on Transformers have achieved remarkable successes. The self-attention mechanism of Transformers for computer vision and natural language processing suffers from the oversmoothing problem, i.e., hidden representations becoming similar to tokens. In the SR domain, we, for the first time, show that the same problem occurs. We present pioneering investigations that reveal the low-pass filtering nature of self-attention in the SR, which causes oversmoothing. To this end, we propose a novel method called Beyond Self-Attention for Sequential Recommendation (BSARec), which leverages the Fourier transform to i) inject an inductive bias by considering fine-grained sequential patterns and ii) integrate low and high-frequency information to mitigate oversmoothing. Our discovery shows significant advancements in the SR domain and is expected to bridge the gap for existing Transformer-based SR models. We test our proposed approach through extensive experiments on 6 benchmark datasets. The experimental results demonstrate that our model outperforms 7 baseline methods in terms of recommendation performance. Our code is available at https://github.com/yehjin-shin/BSARec.

Timeliness and contextual accuracy of recommendations are increasingly important when delivering contemporary digital marketing experiences. Conventional recommender systems (RS) suggest relevant but time-invariant items to users by accounting for their past purchases. These recommendations only map to customers’ general preferences rather than a customer’s specific needs immediately preceding a purchase. In contrast, RSs that consider the order of transactions, purchases, or experiences to measure evolving preferences can offer more salient and effective recommendations to customers: Sequential RSs not only benefit from a better behavioral understanding of a user’s current needs but also provide better predictive power. In this paper, we demonstrate and rank the effectiveness of a sequential recommendation system by utilizing a production dataset of over 2.7 million credit card transactions for 46K cardholders. The methodfirst employs an autoencoder on raw transaction data and submits observed transaction encodings to a GRU-based sequential model. The sequential model produces a MAP@1 metric of 47% on the out-of-sample test set, in line with existing research. We also discuss implications for embedding real-timepredictions using the sequential RS into Nexus, a scalable, lowlatency, event-based digital experience architecture.

Sequential recommendation (SR) aims to predict the subsequent behaviors of users by understanding their successive historical behaviors. Recently, some methods for SR are devoted to alleviating the data sparsity problem (i.e., limited supervised signals for training), which take account of contrastive learning to incorporate self-supervised signals into SR. Despite their achievements, it is far from enough to learn informative user/item embeddings due to the inadequacy modeling of complex collaborative information and co-action information, such as user-item relation, user-user relation, and item-item relation. In this paper, we study the problem of SR and propose a novel multi-level contrastive learning framework for sequential recommendation, named MCLSR. Different from the previous contrastive learning-based methods for SR, MCLSR learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i.e., interest- and feature-level). Specifically, the interest-level contrastive mechanism jointly learns the collaborative information with the sequential transition patterns, and the feature-level contrastive mechanism re-observes the relation between users and items via capturing the co-action information (i.e., co-occurrence). Extensive experiments on four real-world datasets show that the proposed MCLSR outperforms the state-of-the-art methods consistently.