Recent advances in explainable recommendation have explored the integration of language models to analyze natural language rationales for user–item interactions. Despite their potential, existing methods often rely on ID-based representations that obscure semantic meaning and impose structural constraints on language models, thereby limiting their applicability in open-ended scenarios. These challenges are intensified by the complex nature of real-world interactions, where diverse user intents are entangled and collaborative signals rarely align with linguistic semantics. To overcome these limitations, we propose BEAT, a unified and transferable framework that tokenizes user and item behaviors into discrete, interpretable sequences. We construct a behavior vocabulary via a vector-quantized autoencoding process that disentangles macro-level interests and micro-level intentions from graph-based representations. We then introduce multi-level semantic supervision to bridge the gap between behavioral signals and language space. A semantic alignment regularization mechanism is designed to embed behavior tokens directly into the input space of frozen language models. Experiments on three public datasets show that BEAT improves zero-shot recommendation performance while generating coherent and informative explanations. Further analysis demonstrates that our behavior tokens capture fine-grained semantics and offer a plug-and-play interface for integrating complex behavior patterns into large language models.

Mobile signaling data has been used to discover comprehensive and fine-grained patterns of individual travel activities. However, mobile signaling data often exhibits notable data quality issues caused by systematic factors (e.g., weather, construction, congestion) or individual factors (e.g., low battery, poor reception). Improving the quality of signaling data is a crucial yet challenging task due to irregular spatiotemporal intervals and complex inter-correlations among base stations. In this paper, we formalize the augmentation of signaling data as a base station location reconstruction task. We propose a location-aware transformer structure named SigFormer, which employs self-supervised learning to reconstruct the location information of missing base stations based on the sampled signaling sequence. Specifically, we first design a Continuous Signaling Encoder to model irregularly sampled signaling sequences and encode interval information generated by base station transitions. Then, we learn the Base Station Embedding to describe implicit features of base stations and design a Neighbor Region Encoder to incorporate geographic information as an auxiliary for base station representations. Finally, through an attention-based encoder-decoder framework, we aggregate location information and base station features, employing sequence learning to capture spatiotemporal dependencies and reconstruct base station locations. Experiment results on real-world datasets indicate that our method outperforms existing approaches for location reconstruction. The proposed method originates from practical demands in telecom systems, addressing the challenge of missing and abnormal base station locations by modeling spatiotemporal transition patterns in signaling data. Our approach supports various tasks in practical applications, including imputing missing base station locations and correcting anomalous locations, effectively improving the quality of signaling data.

Information diffusion prediction, aimed at estimating future interacting users for a given content, is crucial for various applications on online social platforms. Recently, methods based on dynamic graph learning have achieved superior performance. However, these methods often face scalability issues due to their full-neighbor aggregation, which requires loading the whole diffusion graph, making them impractical for large graphs. While improving model scalability through sampling is an immediate approach, it is challenging on the diffusion graph due to various user dependencies (i.e., the temporal and structural correlations of user–item interactions). To address this problem, we propose a new model named ASDIP, which performs adaptive sampling on the diffusion graph. Specifically, ASDIP employs multiple sampling strategies to extract walks from the diffusion graph, each identifying a representative user dependency by sampling walks that satisfy a specific temporal constraint. Next, the walks sampled by different strategies are first mapped into distinct strategy-specific user representations and then merged into a unified user representation, adaptively fusing the information obtained from different strategies. Finally, a cascade representation learning module is proposed to generate cascade representations based on user representations and interaction timestamps. Experimental results validate the effectiveness and scalability of ASDIP.

Dynamic graph learning has attracted much attention in recent years due to the fact that most of the real-world graphs are dynamic and evolutionary.As a result, many dynamic learning methods have been proposed to cope with the changes of node states over time.Among these studies, a critical issue is how to update the representations of nodes when new temporal events are observed.In this paper, we provide a novel memory structure -Memory Map (MemMap) for this problem.MemMap is an adaptive and evolutionary latent memory space, where each cell corresponds to an evolving "topic" of the dynamic graph.Moreover, the representation of a node is generated from its semantically correlated memory cells, rather than linked neighbors of the node.We have conducted experiments on real-world datasets and compared our method with the SOTA ones.It can be concluded that: 1) By constructing an adaptive and evolving memory structure during the dynamic learning process, our method can capture the dynamic graph changes, and the learned MemMap is actually a compact evolving structure organized according to the latent "topics" of the graph nodes.2) Our research suggests that it is a more effective and efficient way to generate node representations from a latent semantic space (like MemMap in our method) than from directly connected neighbors (like most of the previous graph learning methods).The reason is that the number of memory cells in latent space could be much smaller than the number of nodes in a real-world graph, and the representation learning process could well balance the global and local message passing by leveraging the semantic similarity of graph nodes via the correlated memory cells.

Spatiotemporal data mining plays an important role in air quality monitoring, crowd flow modeling, and climate forecasting. However, the originally collected spatiotemporal data in real-world scenarios is usually incomplete due to sensor failures or transmission loss. Spatiotemporal imputation aims to fill the missing values according to the observed values and the underlying spatiotemporal dependence of them. The previous dominant models impute missing values autoregressively and suffer from the problem of error accumulation. As emerging powerful generative models, the diffusion probabilistic models can be adopted to impute missing values conditioned by observations and avoid inferring missing values from inaccurate historical imputation. However, the construction and utilization of conditional information are inevitable challenges when applying diffusion models to spatiotemporal imputation. To address above issues, we propose a conditional diffusion framework for spatiotemporal imputation with enhanced prior modeling, named PriSTI. Our proposed framework provides a conditional feature extraction module first to extract the coarse yet effective spatiotemporal dependencies from conditional information as the global context prior. Then, a noise estimation module transforms random noise to realistic values, with the spatiotemporal attention weights calculated by the conditional feature, as well as the consideration of geographic relationships. PriSTI outperforms existing imputation methods in various missing patterns of different real-world spatiotemporal data, and effectively handles scenarios such as high missing rates and sensor failure. The implementation code is available at https://github.com/LMZZML/PriSTI.

Popularity prediction, which aims to forecast how many users would like to interact with a target item or online content in the future, can help online shopping or social media platforms to identify popular items or digital contents. Many efforts have been made to study how the multi-faceted factors, such as item features, user preferences, and social influence, affect user-item interactions, but little work has focused on the evolutionary dynamics of these factors for individuals or groups. In that light, this paper develops a community-based dynamic graph learning method for popularity prediction. First, a dynamic graph learning framework is proposed to maintain a dynamic representation for each item or user entity and update the representations according to the newly observed user-item interactions. Second, a community detection module is designed to capture the evolving community structures and identify the most influential nodes. More importantly, our framework leverages a community-level message passing during the learning process to balance local and global information propagation. Finally, we predict the popularity of the target item or online content based on the learned representations. Our experimental results based on three real-world datasets demonstrate that the proposed method achieves better performance than the baselines. Our method could not only model the changes in a user's preferences, but also capture how the communities evolve over time.

Forecasting traffic flow is an important task in urban areas, and a large number of methods have been proposed for traffic flow prediction. However, most of the existing methods follow a general technical route to aggregate historical information spatially and temporally. In this paper, we propose a different approach for traffic flow prediction. Our major motivation is to more effectively incorporate various intrinsic patterns in real-world traffic flows, such as fixed spatial distributions, topological correlations, and temporal periodicity. Along this line, we propose a novel autoencoder-based traffic flow prediction method, named Spatio-Temporal AutoEncoder (ST-AE). The core of our method is an autoencoder specially designed to learn the intrinsic patterns from traffic flow data, and encode the current traffic flow information into a low-dimensional representation. The prediction is made by simply projecting the current hidden states to the future hidden states, and then reconstructing the future traffic flows with the trained autoencoder. We have conducted extensive experiments on four real-world data sets. Our method outperforms existing methods in several settings, particularly for long-term traffic flow prediction.

Due to the increasing diversification of urban transportation modes, many urban areas have the problem of unbalanced traffic demand, which makes accurate prediction of traffic demand very important. However, most of the existing studies focus on improving the prediction accuracy of traffic demand on the single spatial relationship of a single traffic mode, ignoring the diversity of spatial relationships and the heterogeneity of transportation stations in the traffic network. In this paper, we propose a Co-Modal Graph Attention neTwork(CMGAT) framework to uncover the impact of different spatial relationships and traffic mode interactions on traffic demand. Specifically, we first utilize a feature embedding block to capture the semantic information from several features. Then, a multiple traffic graphs-based spatial attention mechanism and a multiple time periods-based temporal attention mechanism are proposed to capture spatial and temporal dependencies in multi-mode traffic demands. Moreover, an output layer is provided to incorporate the hidden states and raw time sequences to predict future traffic demand. Finally, we conduct experiments on two real-world datasets, NYC Bike and NYC Taxi, and the results not only demonstrate the superiority of our model, but also indicate the necessity of considering multiple spatial relationships and traffic modes.

Transportation demand prediction is a classic problem in intelligent transportation research. However, most exist studies have been focused on improving the prediction accuracy in a single demand mode, and there is a lack of understanding of the impact of multiple transportation modes. To this paper, we aim to uncover the interactions of multiple transportation modes and develop a co-prediction method for multimodal transportation demand prediction. Specifically, we first propose a self-learned spatial graph construction method, which automatically learns spatial dependencies of both homogeneous and heterogeneous transportation stations, and then constructs a mode-free spatial dependence graph of the studied transportation stations. Then, a spatiotemporal convolution module is provided to update the state of each station spatially and temporally according to its neighbor stations on the self-learned spatial graph. Moreover, we design an output layer to map the hidden state of each station to the demands of multimodal transportation stations. Finally, experimental results on real-world data have not only validated the effectiveness of the proposed method, but also revealed that co-prediction of multimodal transportation demands could always result in higher prediction performances than single-mode prediction methods as it takes the interactions of multiple transportation modes into account.