GenAI4ELab/papercli-papers huggingface dataset:
Scalable Trajectory-User Linking with Dual-Stream Representation Networks
Authors: Hao Zhang; Wei Chen; Xingyu Zhao; Jianpeng Qi; Guiyuan Jiang; Yanwei Yu
Venue: AAAI 2025 (main)

Abstract: Trajectory-user linking (TUL) aims to match anonymous trajectories to the most likely users who generated them, offering benefits for a wide range of real-world spatio-temporal applications. However, existing TUL methods are limited by high model complexity and poor learning of the effective representations of trajectories, rendering them ineffective in handling large-scale user trajectory data.In this work, we propose a novel Scalable Trajectory-User Linking with dual-stream representation networks for large-scale TUL problem, named ScaleTUL Specifically, ScaleTUL generates two views using temporal and spatial augmentations to exploit supervised contrastive learning framework to effectively capture the irregularities of trajectories. In each view, a dual-stream trajectory encoder consisting of a long-term encoder and a short-term encoder is designed to learn the unified representations of trajectories that fuses different temporal-spatial dependencies. Then, a TUL layer is used to associate the trajectories with the corresponding users in the representation space using a two-stage training model.Experimental results on check-in mobility datasets from three real-world cities and the nationwide U.S. demonstrate the superiority of ScaleTUL over state-of-the-art baselines for large-scale TUL tasks.
Expand Horizon: Graph Out-of-Distribution Generalization via Multi-Level Environment Inference
Authors: Jiaqiang Zhang; Songcan Chen
Venue: AAAI 2025 (main)

Abstract: Graph neural networks (GNNs) are widely used for node classification tasks, but when encountering distribution shifts due to environmental change in real-world scenarios, they tend to learn unstable correlations between features and labels. To overcome this dilemma, a powerful class of approaches views the environment as the root cause of those unstable correlations, thereby their key focus is to infer the environment involved, enabling the model to avoid capturing environment-sensitive correlations. However, their inferences rely solely on the single-level information from one low-hop ego-graph, neglecting both global information and multi-granularity information in local ego-graphs with different hops. Although applying deeper GNNs on the high-hop ego-graph could capture global information, it will bring the side effect of over-smoothing node representations. To tackle these issues, we propose a novel Multi-Level Environment Inference model named MLEI, which effectively broadens the horizon of training GNNs under node-level distribution shifts. Specifically, MLEI first leverages a linear graph transformer to surpass the scope of ego-graph, efficiently enabling high-level global environment inference. This global environment is in turn used as an overview to assist layer-by-layer environment inference on local multi-hop ego-graphs. Finally, we combine the environment from global and local views and utilize the designed objective function to capture stable predictive patterns. Extensive experiments on real-world datasets demonstrate that our model achieves satisfactory performance compared with the state-of-the-art methods under various distribution shifts.
Lightweight Yet Fine-Grained: A Graph Capsule Convolutional Network with Subspace Alignment for Shared-Account Sequential Recommendation
Authors: Jinyu Zhang; Zhongying Zhao; Chao Li; Yanwei Yu
Venue: AAAI 2025 (main)

Abstract: Shared-account Sequential Recommendation (SSR) aims to provide personalized recommendations for accounts shared by multiple users with varying sequential preferences. Previous studies on SSR struggle to capture the fine-grained associations between interactions and different latent users within the shared account's hybrid sequences. Moreover, most existing SSR methods (e.g., RNN-based or GCN-based methods) have quadratic computational complexities, hindering the deployment of SSRs on resource-constrained devices. To this end, we propose a Lightweight Graph Capsule Convolutional Network with subspace alignment for shared-account sequential recommendation, named LightGC2N. Specifically, we devise a lightweight graph capsule convolutional network. It facilitates the fine-grained matching between interactions and latent users by attentively propagating messages on the capsule graphs. Besides, we present an efficient subspace alignment method. This method refines the sequence representations and then aligns them with the finely clustered preferences of latent users. The experimental results on four real-world datasets indicate that LightGC2N outperforms nine state-of-the-art methods in accuracy and efficiency.
L3TC: Leveraging RWKV for Learned Lossless Low-Complexity Text Compression
Authors: Junxuan Zhang; Zhengxue Cheng; Yan Zhao; Shihao Wang; Dajiang Zhou; Guo Lu; Li Song
Venue: AAAI 2025 (main)

Abstract: Learning-based probabilistic models can be combined with an entropy coder for data compression. However, due to the high complexity of learning-based models, their practical application as text compressors has been largely overlooked. To address this issue, our work focuses on a low-complexity design while maintaining compression performance. We introduce a novel Learned Lossless Low-complexity Text Compression method (L3TC). Specifically, we conduct extensive experiments demonstrating that RWKV models achieve the fastest decoding speed with a moderate compression ratio, making it the most suitable backbone for our method. Second, we propose an outlier-aware tokenizer that uses a limited vocabulary to cover frequent tokens while allowing outliers to bypass the prediction and encoding. Third, we propose a novel high-rank reparameterization strategy that enhances the learning capability during training without increasing complexity during inference. Experimental results validate that our method achieves 48% bit saving compared to gzip compressor. Besides, L3TC offers compression performance comparable to other learned compressors, with a 50x reduction in model parameters. More importantly, L3TC is the fastest among all learned compressors, providing real-time decoding speeds up to megabytes per second.
Trigger3:Refining Query Correction via Adaptive Model Selector
Authors: Kepu Zhang; Zhongxiang Sun; Xiao Zhang; Xiaoxue Zang; Kai Zheng; Yang Song; Jun Xu
Venue: AAAI 2025 (main)

Abstract: In search scenarios, user experience can be hindered by erroneous queries due to typos, voice errors, or knowledge gaps. Therefore, query correction is crucial for search engines. Current correction models, usually small models trained on specific data, often struggle with queries beyond their training scope or those requiring contextual understanding. While the advent of Large Language Models (LLMs) offers a potential solution, they are still limited by their pre-training data and inference cost, particularly for complex queries, making them not always effective for query correction. To tackle these, we propose Trigger3, a large-small model collaboration framework that integrates the traditional correction model and LLM for query correction, capable of adaptively choosing the appropriate correction method based on the query and the correction results from the traditional correction model and LLM. Trigger3 first employs a correction trigger to filter out correct queries. Incorrect queries are then corrected by the traditional correction model. If this fails, an LLM trigger is activated to call the LLM for correction. Finally, for queries that no model can correct, a fallback trigger decides to return the original query. Extensive experiments demonstrate Trigger3 outperforms correction baselines while maintaining efficiency.
Teacher-guided Edge Discriminator for Personalized Graph Masked Autoencoder
Authors: Qiqi Zhang; Chao Li; Zhongying Zhao
Venue: AAAI 2025 (main)

Abstract: Graph Masked AutoEncoder (GMAE) has recently attracted vast interest in handling graph-related tasks by adopting the 'masking-reconstruction' learning paradigm. Most existing GMAE-based methods adhere to the homophily assumption, i.e., connected nodes share the same attributes or labels. However, this assumption is not always right because most graphs from real-world applications are mixed by both homophilic and heterophilic edges. Therefore, it is necessary to distinguish them to improve the representative ability of GMAE. In this paper, we propose a teacher-guided edge discriminator for the personalized graph masked autoencoder (TEDMAE). Specifically, we design a teacher-guided edge discriminator that distinguishes homophilic and heterophilic edges by leveraging the embeddings from teacher models with structure and attribute knowledge. Then, we present a personalized graph masked autoencoder that individually tailors the masking, encoding, and reconstruction processes for each graph. Finally, we optimize the model by minimizing two types of loss functions, i.e., the scaled cosine error (SCE) loss and the InfoNCE loss. Experimental results on 10 datasets demonstrate the superior performance of TEDMAE on the tasks of node classification and node clustering.
Integrating Large Language Models and Möbius Group Transformations for Temporal Knowledge Graph Embedding on the Riemann Sphere
Authors: Sensen Zhang; Xun Liang; Simin Niu; Zhendong Niu; Bo Wu; Gengxin Hua; Long Wang; Zhenyu Guan; Hanyu Wang; Xuan Zhang; Zhiyu Li; Yuefeng Ma
Venue: AAAI 2025 (main)

Abstract: The significance of Temporal Knowledge Graphs (TKGs) in Artificial Intelligence (AI) lies in their capacity to incorporate time-dimensional information, support complex reasoning and prediction, optimize decision-making processes, enhance the accuracy of recommendation systems, promote multimodal data integration, and strengthen knowledge management and updates. This provides a robust foundation for various AI applications. To effectively learn and apply both static and dynamic temporal patterns for reasoning, a range of embedding methods and large language models (LLMs) have been proposed in the literature. However, these methods often rely on a single underlying embedding space, whose geometric properties severely limit their ability to model intricate temporal patterns, such as hierarchical and ring structures. To address this limitation, this paper proposes embedding TKGs into projective geometric space and leverages LLMs technology to extract crucial temporal node information, thereby constructing the 5EL model. By embedding TKGs into projective geometric space and utilizing Möbius Group transformations, we effectively model various temporal patterns. Subsequently, LLMs technology is employed to process the trained TKGs. We adopt a parameter-efficient fine-tuning strategy to align LLMs with specific task requirements, thereby enhancing the model's ability to recognize structural information of key nodes in historical chains and enriching the representation of central entities. Experimental results on five advanced TKG datasets demonstrate that our proposed 5EL model significantly outperforms existing models.
Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution
Authors: Shengjia Zhang; Jiawei Chen; Changdong Li; Sheng Zhou; Qihao Shi; Yan Feng; Chun Chen; Can Wang
Venue: AAAI 2025 (main)

Abstract: Loss functions play a pivotal role in optimizing recommendation models. Among various loss functions, Softmax Loss (SL) and Cosine Contrastive Loss (CCL) are particularly effective. Their theoretical connections and differences warrant in-depth exploration. This work conducts comprehensive analyses of these losses, yielding significant insights: 1) Common strengths --- both can be viewed as augmentations of traditional losses with Distributional Robust Optimization (DRO), enhancing robustness to distributional shifts; 2) Respective limitations --- stemming from their use of different distribution distance metrics in DRO optimization, SL exhibits high sensitivity to false negative instances, whereas CCL suffers from low data utilization. To address these limitations, this work proposes a new loss function, DrRL, which generalizes SL and CCL by leveraging Rényi-divergence in DRO optimization. DrRL incorporates the advantageous structures of both SL and CCL, and can be demonstrated to effectively mitigate their limitations. Extensive experiments have been conducted to validate the superiority of DrRL on both recommendation accuracy and robustness.
ExcluIR: Exclusionary Neural Information Retrieval
Authors: Wenhao Zhang; Mengqi Zhang; Shiguang Wu; Jiahuan Pei; Zhaochun Ren; Maarten de Rijke; Zhumin Chen; Pengjie Ren
Venue: AAAI 2025 (main)

Abstract: Exclusion is an important and universal linguistic skill that humans use to express what they do not want. There is little research on exclusionary retrieval, where users express what they do not want to be part of the results produced for their queries. We investigate the scenario of exclusionary retrieval in document retrieval for the first time. We present ExcluIR, a set of resources for exclusionary retrieval, consisting of an evaluation benchmark and a training set for helping retrieval models to comprehend exclusionary queries. The evaluation benchmark includes 3,452 high-quality exclusionary queries, each of which has been manually annotated. The training set contains 70,293 exclusionary queries, each paired with a positive document and a negative document. We conduct detailed experiments and analyses, obtaining three main observations: (i) existing retrieval models with different architectures struggle to comprehend exclusionary queries effectively; (ii) although integrating our training data can improve the performance of retrieval models on exclusionary retrieval, there still exists a gap compared to human performance; and (iii) generative retrieval models have a natural advantage in handling exclusionary queries.
A Lightweight Sparse Interaction Network for Time Series Forecasting
Authors: Xu Zhang; Qitong Wang; Peng Wang; Wei Wang
Venue: AAAI 2025 (main)

Abstract: Recent work shows that linear models can outperform several transformer models in long-term time-series forecasting (TSF). However, instead of explicitly performing temporal interaction through self-attention, linear models implicitly perform it based on stacked MLP structures, which may be insufficient in capturing the complex temporal dependencies and their performance still has potential for improvement. To this end, we propose a Lightweight Sparse Interaction Network (LSINet) for TSF task. Inspired by the sparsity of self-attention, we propose a Multihead Sparse Interaction
Mechanism (MSIM). Different from self-attention, MSIM learns the important connections between time steps through sparsity-induced Bernoulli distribution to capture temporal dependencies for TSF. The sparsity is ensured by the proposed self-adaptive regularization loss. Moreover, we observe the shareability of temporal interactions and propose to perform Shared Interactions Learning (SIL) for MSIM to further enhance efficiency and improve convergence. LSINet is a linear model comprising only MLP structures with low overhead and equipped with explicit temporal interaction mechanisms. Extensive experiments on public datasets show that LSINet achieves both higher accuracy and better efficiency than advanced linear models and transformer models in TSF tasks.
Multi-type MOOCs Recommendation: Leveraging Deep Multi-Relational Representation and Hierarchical Reasoning
Authors: Ye Zhang; Yanqi Gao; Dongjie Wang; Yupeng Zhou; Jinlong He; Zhaoyang Sun; Minghao Yin
Venue: AAAI 2025 (main)

Abstract: Massive open online courses (MOOCs) recommendation provides online courses tailored to learners' individual preferences. Existing literature is limited by: 1) Ignoring the interrelations among courses, knowledge concepts, and videos, which leads to suboptimal recommendation performance; 2) Neglecting the hierarchical interactions between learners and components like courses, knowledge concepts, and videos, which makes it difficult to capture learners' intentions accurately. To address them, we propose a novel multi-type MOOCs recommendation framework, which enables multi-type educational content recommendations. This framework includes two important components: multi-relational representation and hierarchical reasoning. Regarding multi-relational representation, we first create two static course-relational and knowledge concept-relational graphs based on domain knowledge and construct a dynamic video-relational graph using learners' browsing historical sequences. Then, we capture the interactions among different components by learning the corresponding embeddings via graph neural networks. Regarding hierarchical reasoning, we implement a hierarchical beam search strategy to narrow down the candidate courses, knowledge concepts, and videos by calculating joint probability. Finally, we introduce an optional layer to increase the diversity and reasonableness of video recommendations by estimating learners' intentions. Extensive experiments are conducted to show the effectiveness, robustness, and interpretability of our method.
Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation
Authors: Yichi Zhang; Zhuo Chen; Lingbing Guo; Yajing Xu; Binbin Hu; Ziqi Liu; Wen Zhang; Huajun Chen
Venue: AAAI 2025 (main)

Abstract: Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given multi-modal knowledge graphs (MMKG), collaboratively leveraging structural information from the triples and multi-modal information of the entities to overcome the inherent incompleteness. Existing MMKGC methods usually extract multi-modal features with pre-trained models and employ fusion modules to integrate multi-modal features for the entities. This often results in coarse handling of multi-modal entity information, overlooking the nuanced, fine-grained semantic details and their complex interactions. To tackle this shortfall, we introduce a novel framework MyGO to tokenize, fuse, and augment the fine-grained multi-modal representations of entities and enhance the MMKGC performance. Motivated by the tokenization technology, MyGO tokenizes multi-modal entity information as fine-grained discrete tokens and learns entity representations with a cross-modal entity encoder. To further augment the multi-modal representations, MyGO incorporates fine-grained contrastive learning to highlight the specificity of the entity representations. Experiments on standard MMKGC benchmarks reveal that our method surpasses 19 of the latest models, underlining its superior performance.
Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective
Authors: Zhongjian Zhang; Mengmei Zhang; Xiao Wang; Lingjuan Lyu; Bo Yan; Junping Du; Chuan Shi
Venue: AAAI 2025 (main)

Abstract: To preserve user privacy in recommender systems, federated recommendation (FR) based on federated learning (FL) emerges, keeping the personal data on the local client and updating a model collaboratively. Unlike FL, FR has a unique sparse aggregation mechanism, where the embedding of each item is updated by only partial clients, instead of full clients in a dense aggregation of general FL. Recently, as an essential principle of FL, model security has received increasing attention, especially for Byzantine attacks, where malicious clients can send arbitrary updates. The problem of exploring the Byzantine robustness of FR is particularly critical since in the domains applying FR, e.g., e-commerce, malicious clients can be injected easily by registering new accounts. However, existing Byzantine works neglect the unique sparse aggregation of FR, making them unsuitable for our problem. Thus, we make the first effort to investigate Byzantine attacks on FR from the perspective of sparse aggregation, which is non-trivial: it is not clear how to define Byzantine robustness under sparse aggregations and design Byzantine attacks under limited knowledge/capability. In this paper, we reformulate the Byzantine robustness under sparse aggregation by defining the aggregation for a single item as the smallest execution unit. Then we propose a family of effective attack strategies, named Spattack, which exploit the vulnerability in sparse aggregation and are categorized along the adversary's knowledge and capability. 
Extensive experimental results demonstrate that Spattack can effectively prevent convergence and even break down defenses under a few malicious clients, raising alarms for securing FR systems.
LLM-Powered User Simulator for Recommender System
Authors: Zijian Zhang; Shuchang Liu; Ziru Liu; Rui Zhong; Qingpeng Cai; Xiangyu Zhao; Chunxu Zhang; Qidong Liu; Peng Jiang
Venue: AAAI 2025 (main)

Abstract: User simulators can rapidly generate a large volume of timely user behavior data, providing a testing platform for reinforcement learning-based recommender systems, thus accelerating their iteration and optimization. However, prevalent user simulators generally suffer from significant limitations, including the opacity of user preference modeling and the incapability of evaluating simulation accuracy. In this paper, we introduce an LLM-powered user simulator to simulate user engagement with items in an explicit manner, thereby enhancing the efficiency and effectiveness of reinforcement learning-based recommender systems training. Specifically, we identify the explicit logic of user preferences, leverage LLMs to analyze item characteristics and distill user sentiments, and design a logical model to imitate real human engagement. By integrating a statistical model, we further enhance the reliability of the simulation, proposing an ensemble model that synergizes logical and statistical insights for user interaction simulations. Capitalizing on the extensive knowledge and semantic generation capabilities of LLMs, our user simulator faithfully emulates user behaviors and preferences, yielding high-fidelity training data that enrich the training of recommendation algorithms. We establish quantifying and qualifying experiments on five datasets to validate the simulator's effectiveness and stability across various recommendation scenarios.
UniTR: A Unified Framework for Joint Representation Learning of Trajectories and Road Networks
Authors: Jie Zhao; Chao Chen; Yuanshao Zhu; Mingyu Deng; Yuxuan Liang
Venue: AAAI 2025 (main)

Abstract: Representation learning of urban spatial-temporal data is fundamental and critical, serving a wide range of intelligent applications. Given that road networks and trajectories are inherently interrelated, their joint representation learning can significantly enhance the accuracy and utility of these applications. However, effectively learning joint representations for these two types of data remains challenging, particularly due to the complexities of interaction modeling and cross-scale optimization. To this end, we propose a unified framework, named UniTR, for joint representation learning of road networks and trajectories. Specifically, we first design a hierarchical propagation mechanism to model the complex many-to-many interactions between road networks and trajectories, thereby generating informative embeddings. Then, a triple-level contrastive optimization module is incorporated to systematically select valid positive and negative samples, further refining the embeddings.  Experiments conducted on real-world datasets from two cities clearly demonstrate the effectiveness and superiority of UniTR.