Yuxia Wu, Shujie Li, Yuan Fang, Chuan Shi. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Modeling dynamic graphs, such as those found in social networks, recommendation systems, and e-commerce platforms, is crucial for capturing evolving relationships and delivering relevant insights over time. Traditional approaches primarily rely on graph neural networks with temporal components or sequence generation models, which often focus narrowly on the historical context of target nodes. This limitation restricts the ability to adapt to new and emerging patterns in dynamic graphs. To address this challenge, we propose a novel framework, Retrieval-Augmented Generation for Dy namic Graph modeling (RAG4DyG ), which enhances dynamic graph predictions by incorporating contextually and temporally relevant examples from broader graph structures. Our approach includes a time- and context-aware contrastive learning module to identify high-quality demonstrations and a graph fusion strategy to effectively integrate these examples with historical contexts. The proposed framework is designed to be effective in both transductive and inductive scenarios, ensuring adaptability to previously unseen nodes and evolving graph structures. Extensive experiments across multiple real-world datasets demonstrate the effectiveness of RAG4DyG in improving predictive accuracy and adaptability for dynamic graph modeling. The code and datasets are publicly available at https://github.com/YuxiaWu/RAG4DyG.

This article presents an optimal formation control strategy for multiagent systems based on a reinforcement learning (RL) technique, considering prescribed performance and unknown nonlinear faults. To optimize the control performance, an RL strategy is introduced based on the identifier-critic–actor-disturbance structure and backstepping frame. The identifier, critic, actor, and disturbance neural networks (NNs) are employed to estimate unknown dynamics, assess system performance, carry out control actions, and derive the worst disturbance strategy, respectively. With the scheme, the persistent excitation requirements are removed by adopting simplified NNs updating laws, which are derived using the gradient descent method toward designed positive functions instead of the square of Bellman residual. For achieving the desired error precision within the prescribed time, a constraining function and an error transformation scheme are employed. In addition, to enhance the system’s robustness, a fault observer is utilized to compensate for the impact of the unknown nonlinear faults. The stability of the closed-loop system is assured, while the prescribed performance is realized. Finally, simulation examples validate the effectiveness of the proposed optimal control strategy.

In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for enhancing the adaptability and robustness of conversational agents.Traditional models rely on static, predefined ontologies, limiting their ability to handle new and unforeseen user needs.This survey paper provides a comprehensive review of the state-of-the-art techniques in On-Exp for conversational understanding.It categorizes the existing literature into three main areas: (1) New Intent Discovery, (2) New Slot-Value Discovery, and (3) Joint OnExp.By examining the methodologies, benchmarks, and challenges associated with these areas, we highlight several emerging frontiers in OnExp to improve agent performance in real-world scenarios and discuss their corresponding challenges.This survey aspires to be a foundational reference for researchers and practitioners, promoting further exploration and innovation in this crucial domain.

Dynamic graph modeling is crucial for understanding complex structures in web graphs, spanning applications in social networks, recommender systems, and more. Most existing methods primarily emphasize structural dependencies and their temporal changes. However, these approaches often overlook detailed temporal aspects or struggle with long-term dependencies. Furthermore, many solutions overly complicate the process by emphasizing intricate module designs to capture dynamic evolutions. In this work, we harness the strength of the Transformer's self-attention mechanism, known for adeptly handling long-range dependencies in sequence modeling. Our approach offers a simple Transformer model, called SimpleDyG, tailored for dynamic graph modeling without complex modifications. We re-conceptualize dynamic graphs as a sequence modeling challenge and introduce a novel temporal alignment technique. This technique not only captures the inherent temporal evolution patterns within dynamic graphs but also streamlines the modeling process of their evolution. To evaluate the efficacy of SimpleDyG, we conduct extensive experiments on four real-world datasets from various domains. The results demonstrate the competitive performance of SimpleDyG in comparison to a series of state-of-the-art approaches despite its simple design.

Existing task-oriented conversational systems heavily rely on domain ontologies with pre-defined slots and candidate values. In practical settings, these prerequisites are hard to meet, due to the emerging new user requirements and ever-changing scenarios. To mitigate these issues for better interaction performance, there are efforts working towards detecting out-of-vocabulary values or discovering new slots under unsupervised or semi-supervised learning paradigms. However, overemphasizing on the conversation data patterns alone induces these methods to yield noisy and arbitrary slot results. To facilitate the pragmatic utility, real-world systems tend to provide a stringent amount of human labeling quota, which offers an authoritative way to obtain accurate and meaningful slot assignments. Nonetheless, it also brings forward the high requirement of utilizing such quota efficiently. Hence, we formulate a general new slot discovery task in an information extraction fashion and incorporate it into an active learning framework to realize human-in-the-loop learning. Specifically, we leverage existing language tools to extract value candidates where the corresponding labels are further leveraged as weak supervision signals. Based on these, we propose a bi-criteria selection scheme which incorporates two major strategies, namely, uncertainty-based and diversity-based sampling to efficiently identify terms of interest. We conduct extensive experiments on several public datasets and compare with a bunch of competitive baselines to demonstrate the effectiveness of our method.

Existing point-of-interest (POI) recommendation methods only show the direct recommendation results and lack the proper reasons for recommendation. In recent years, explainable recommendation has become an increasingly important subfield in recommendation systems. The aim of explainable recommendation is to provide a reason why an item is recommended to a user. In this way, it helps to improve the transparency, persuasiveness and user satisfaction of recommendation systems. The explainable recommendation should indicate users' preferences for POIs, such as the category and the price. In addition, to increase the diversity of the results, we take emotional intensity into account in our model to generate more vivid reasons. To this end, we propose a hierarchical attention-based transformer model to generate reasons with specific topics and different emotions. With a hierarchical attention mechanism, we can capture the word-level and attribute-level preferences of users. In addition, we also learn the latent representation of the emotion score to generate diverse recommendation reasons. We evaluate the proposed model on a new real-world dataset collected from three travel service websites. The experimental results demonstrate that our method outperforms the related approaches for reason generation.

Top-k recommendation is a fundamental task in recommendation systems that is generally learned by comparing positive and negative pairs. The contrastive loss (CL) is the key in contrastive learning that has recently received more attention, and we find that it is well suited for top-k recommendations. However, CL is problematic because it treats the importance of the positive and negative samples the same. On the one hand, CL faces the imbalance problem of one positive sample and many negative samples. On the other hand, there are so few positive items in sparser datasets that their importance should be emphasized. Moreover, the other important issue is that the sparse positive items are still not sufficiently utilized in recommendations. Consequently, we propose a new data augmentation method by using multiple positive items (or samples) simultaneously with the CL loss function. Therefore, we propose a multisample-based contrastive loss (MSCL) function that solves the two problems by balancing the importance of positive and negative samples and data augmentation. Based on the graph convolution network (GCN) method, experimental results demonstrate the state-of-the-art performance of MSCL. The proposed MSCL is simple and can be applied in many methods. Our code is available athttps://github.com/haotangxjtu/MSCL.

Conversational recommendation system (CRS) attracts increasing attention in various application domains such as retail and travel. It offers an effective way to capture users' dynamic preferences with multi-turn conversations. However, most current studies center on the recommendation aspect while over-simplifying the conversation process. The negligence of complexity in data structure and conversation flow hinders their practicality and utility. In reality, there exist various relationships among slots and values, while users' requirements may dynamically adjust or change. Moreover, the conversation often involves visual modality to facilitate the conversation. These actually call for a more advanced internal state representation of the dialogue and a proper reasoning scheme to guide the decision making process. In this paper, we explore multiple facets of multimodal conversational recommendation and try to address the above mentioned challenges. In particular, we represent the structured back-end database as a multimodal knowledge graph which captures the various relations and evidence in different modalities. The user preferences expressed via conversation utterances will then be gradually updated to the state graph with clear polarity. Based on these, we train an end-to-end State Graph-based Reasoning model SGR to perform reasoning over the whole state graph. The prediction of our proposed model benefits from the structure of the graph. It not only allows for zero-shot reasoning for items unseen in training conversations, but also provides a natural way to explain the policies. Extensive experiments show that our model achieves better performance compared with existing methods.

Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users’ historical check-in data. Therefore, it is crucial to model both users’ general taste and recent sequential behaviors. Moreover, different users show different dependencies on the two parts. However, most existing methods learn the same dependencies for different users. Besides, the locations and categories of POIs contain different information about users’ preference. However, current researchers always treat them as the same factors or believe that categories determine where to go. To this end, we propose a novel method named Personalized Long- and Short-term Preference Learning (PLSPL) to learn the specific preference for each user. Specially, we combine the long- and short-term preference via user-based linear combination unit to learn the personalized weights on different parts for different users. Besides, the context information such as the category and check-in time is also essential to capture users’ preference. Therefore, in long-term module, we consider the contextual features of POIs in users’ history records and leverage attention mechanism to capture users’ preference. In the short-term module, to better learn the different influences of locations and categories of POIs, we train two LSTM models for location- and category-based sequence, respectively. Then we evaluate the proposed model on two real-world datasets. The experiment results demonstrate that our method outperforms the state-of-art approaches for next POI recommendation.

Sequential recommendations aim to predict the user’s next behaviors items based on their successive historical behaviors sequence. It has been widely applied in lots of online services. However, current sequential recommendations use the adjacent behaviors to capture the features of the sequence, ignoring the features among nonadjacent sequential items and the summarized features of the sequence. To address the above problems, in this paper, we propose an annular-graph attention based sequential recommendation (AGSR) model by exploring user’s long-term and short-term preferences for the personalized sequential recommendation. For user’s short-term preferences, AGSR builds an annular-graph on the sequence of user behavior. Then, AGSR proposes an annular-graph attention applying on the sub annular-graph to explore local features and applying annular-graph attention on entire annular-graph to explore the global features and the skip features. For user’s long-term preferences, the latent factor model are introduced in AGSR. The experimental results on two public datasets show that our model outperforms the state-of-the-art methods.

Viewpoint recommendation can recommend several viewpoints for taking aesthetic photographs of a place-of-interest (POI) and is of great importance for photography assistance. In this paper, we propose a system that can assist a user in choosing good viewpoints for taking high-quality photographs. Our system is based on social media and 3D reconstruction. To reduce the time cost and improve the quality of 3D reconstruction, we propose a weakly supervised object detection method that is used before 3D reconstruction. The camera pose of images is recovered by the subsequent 3D reconstruction pipeline. We use a convolutional neural network (CNN) to extract 2D image features, and we fuse them with 3D camera pose features to learn their relationships to image aesthetics. The trained model is utilized to evaluate the aesthetics of images. Finally, the 3D space of all possible camera poses is divided into 3D grids, and the aesthetics score of each grid is evaluated. We combine the aesthetics and diversity of all viewpoints and recommend several high-quality viewpoints. Experimental results indicate that our approach can help users choose viewpoints that will result in high-quality photographs while maintaining diversity.

When planning a trip, users tend to browse Place-of-Interest (POI) information on the Internet and then depart. Many works aimed at summarizing POIs by visual and textual analysis, while many of them ignored the inter-relationship between different views offered by the community-contributed information. In this paper, we propose a City-POI-LOI (CPL) summarization method to automatically mine POIs from the city-level landmark images. And a Location-Appearance-Semantic-Temporal (LAST) clustering method is proposed to mine the popular viewpoints termed Location-Of-Interest (LOI) in each POI by taking location, appearance, semantic, and temporal feature into consideration. We perform text and image summarization for each LOI, and we further summarize the POIs based on season. We conduct a series of experiments based on DIV400 and ATCF Dataset. Experimental results show the effectiveness of the proposed POI summarization approach.

Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users' historical check-in data. Therefore, it is crucial to model users' general taste and recent sequential behavior. Moreover, the context information such as the category and check-in time is also important to capture user preference. To this end, we propose a long- and short-term preference learning model (LSPL) considering the sequential and context information. In long-term module, we learn the contextual features of POIs and leverage attention mechanism to capture users' preference. In the short-term module, we utilize LSTM to learn the sequential behavior of users. Specifically, to better learn the different influence of location and category of POIs, we train two LSTM models for location-based sequence and category-based sequence, respectively. Then we combine the long and short-term results to recommend next POI for users. At last, we evaluate the proposed model on two real-world datasets. The experiment results demonstrate that our method outperforms the state-of-art approaches for next POI recommendation.