GenAI4ELab/papercli-papers huggingface dataset:
Designing Specialized Two-Dimensional Graph Spectral Filters for Spatial-Temporal Graph Modeling
Authors: Yuxin Chen; Fangru Lin; Jingyi Huo; Hui Yan
Venue: AAAI 2025 (main)

Abstract: Spatial-temporal graph modeling is challenging due to the diverse node interactions across spatial and temporal dimensions. Recent studies typically adopt Graph Neural Networks (GNNs) to perform node-level aggregation at different time steps, acting as a series of low-pass graph spectral filters, for node interaction modeling. However, these filters, confined to the spatial dimension, are ill-suited for processing signals of nodes with inherent spatial-temporal interdependencies. Moreover, oversimplified low-pass filtering fails to fully exploit information from diverse node interactions. To address these issues, we propose a Spatial-Temporal Spectral Graph Neural Network (STSGNN), which designs specialized two-dimensional (2-D) graph spectral filters for comprehensive spatial-temporal graph modeling. First, based on the normalized Laplacian spectrum of spatial and temporal graphs, we extend the existing graph spectral theory from a univariate spatial dimension to a bivariate spatial-temporal dimension through a 2-D Discrete Graph Fourier Transform (2-D DGFT). Then, we leverage the bivariate Bernstein polynomial approximation, with learned basis coefficients, to design 2-D filters with specialized spectral properties for unified spatial-temporal signal filtering. Finally, the filtered signals, with refined spatial-temporal representations, are fed into well-designed pyramidal gated convolution modules to acquire multiple ranges of spatial-temporal dependencies. Experiments on traffic and meteorological prediction tasks demonstrate that STSGNN achieves state-of-the-art performance. Additionally, we visualize the 2-D filters learned from inputs with distinct spatial-temporal characteristics to enhance the model's interpretability.
POI-Enhancer: An LLM-based Semantic Enhancement Framework for POI Representation Learning
Authors: Jiawei Cheng; Jingyuan Wang; Yichuan Zhang; Jiahao Ji; Yuanshao Zhu; Zhibo Zhang; Xiangyu Zhao
Venue: AAAI 2025 (main)

Abstract: POI representation learning plays a crucial role in handling tasks related to user mobility data. Recent studies have shown that enriching POI representations with multimodal information can significantly enhance their task performance. 
Previously, the textual information incorporated into POI representations typically involved only POI categories or check-in content, leading to relatively weak textual features in existing methods. 
In contrast, large language models (LLMs) trained on extensive text data have been found to possess rich textual knowledge.
However leveraging such knowledge to enhance POI representation learning presents two key challenges: first, how to extract POI-related knowledge from LLMs effectively, and second, how to integrate the extracted information to enhance POI representations.
To address these challenges, we propose POI-Enhancer, a portable framework that leverages LLMs to improve POI representations produced by classic POI learning models. We first design three specialized prompts to extract semantic information from LLMs efficiently. Then, the Dual Feature Alignment module enhances the quality of the extracted information, while the Semantic Feature Fusion module preserves its integrity. The Cross Attention Fusion module then fully adaptively integrates such high-quality information into POI representations and Multi-View Contrastive Learning further injects human-understandable semantic information into these representations. Extensive experiments on three real-world datasets demonstrate the effectiveness of our framework, showing significant improvements across all baseline representations.
Descriptive and Discriminative Document Identifiers for Generative Retrieval
Authors: Jiehan Cheng; Zhicheng Dou; Yutao Zhu; Xiaoxi Li
Venue: AAAI 2025 (main)

Abstract: Generative document retrieval is a novel retrieval framework, which represents documents as identifiers (DocID) and retrieves documents by generating DocIDs. It has the advantage of end-to-end optimization over traditional retrieval methods and has attracted much research interest. Nonetheless, the development of efficient and precise DocIDs for document representation remains a pertinent issue within the field. Existing methods for designing DocIDs tend to consider only the relevance of DocIDs to the corresponding documents, while neglecting the ability of the DocIDs to distinguish the corresponding documents from similar ones, which is crucial for the retrieval task. In this paper, we design learnable descriptive and discriminative document Identifiers (D2-DocID) for Generative Retrieval and propose the paired retrieval model D2Gen. The D2-DocID is semantically similar to the corresponding documents (descriptive) and is able to distinguish similar documents (discriminative) in the corpus, thus enhancing retrieval performance. We use a contrastive learning assisted generative retrieval task to enable the model to understand the document and then complete the generative retrieval. We then design a DocID selection method to select DocIDs based on the retrieval model's understanding of the documents. Our experimental results on the MS MARCO and NQ320k dataset illustrate the effectiveness of the approach.
In-context Prompt-augmented Micro-video Popularity Prediction
Authors: Zhangtao Cheng; Jiao Li; Jian Lang; Ting Zhong; Fan Zhou
Venue: AAAI 2025 (main)

Abstract: Micro-video popularity prediction (MVPP) plays a crucial role in various downstream applications. Recently, multimodal methods that integrate multiple modalities to predict the popularity have exhibited impressive performance. However, these methods face several unresolved issues: (1) limited contextual information and (2) incomplete modal semantics. Incorporating relevant videos and performing full fine-tuning on pre-trained models typically achieves powerful capabilities in addressing these issues. However, this paradigm is not optimal due to its weak transferability and scarce downstream data. Inspired by prompt learning, we propose ICPF, a novel In-Context Prompt-augmented Framework to enhance popularity prediction. ICPF maintains a model-agnostic design, facilitating seamless integration with various multimodal fusion models. Specifically, the multi-branch retriever first retrieves similar modal content through within-modality similarities. Next, in-context prompt generator extracts semantic prior features from retrieved videos and generates in-context prompts, enriching pre-trained models with valuable contextual knowledge. Finally, knowledge-augmented predictor captures complementary features including modal semantics and popularity information. Extensive experiments conducted on three real-world datasets demonstrate the superiority of ICPF compared to 14 competitive baselines.
No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods
Authors: Zhengxing Cheng; Yuheng Huang; Zhixuan Zhang; Dan Ou; Qingwen Liu
Venue: AAAI 2025 (main)

Abstract: Given the ubiquity of multi-task in practical systems, Multi-Task Learning (MTL) has found widespread application across diverse domains. In real-world scenarios, these tasks often have different priorities. For instance, In web search, relevance is often prioritized over other metrics, such as click-through rates or user engagement. Existing frameworks pay insufficient attention to the prioritization among different tasks, which typically adjust task-specific loss function weights to differentiate task priorities. However, this approach encounters challenges as the number of tasks grows, leading to exponential increases in hyper-parameter tuning complexity. Furthermore, the simultaneous optimization of multiple objectives can negatively impact the performance of high-priority tasks due to interference from lower-priority tasks.
In this paper, we introduce a novel multi-task learning framework employing Lagrangian Differential Multiplier Methods for step-wise multi-task optimization. It is designed to boost the performance of high-priority tasks without interference from other tasks. Its primary advantage lies in its ability to automatically optimize multiple objectives without requiring balancing hyper-parameters for different tasks, thereby eliminating the need for manual tuning. Additionally, we provide theoretical analysis demonstrating that our method ensures optimization guarantees, enhancing the reliability of the process. We demonstrate its effectiveness through experiments on multiple public datasets and its application in Taobao search, a large-scale industrial search ranking system, resulting in significant improvements across various business metrics.
ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effect
Authors: Seoyoung Cho; Jaesung Hwang; Kwan-Young Bak; Dongha Kim
Venue: AAAI 2025 (main)

Abstract: Outlier detection (OD) is the task of identifying unusual observations (or outliers) from a given or upcoming data by learning unique patterns of normal observations (or inliers). Recently, a study introduced a powerful unsupervised OD (UOD) solver based on a new observation of deep generative models, called inlier-memorization (IM) effect, which suggests that generative models memorize inliers before outliers in early learning stages. In this study, we aim to develop a theoretically principled method to address UOD tasks by maximally utilizing the IM effect. We begin by observing that the IM effect is observed more clearly when the given training data contain fewer outliers. This finding indicates a potential for enhancing the IM effect in UOD regimes if we can effectively exclude outliers from mini-batches when designing the loss function. To this end, we introduce two main techniques: 1) increasing the mini-batch size as the model training proceeds and 2) using an adaptive threshold to calculate the truncated loss function. We theoretically show that these two techniques effectively filter out outliers from the truncated loss function, allowing us to utilize the IM effect to the fullest. Coupled with an additional ensemble technique, we propose our method and term it Adaptive Loss Truncation with Batch Increment (ALTBI). We provide extensive experimental results to demonstrate that ALTBI achieves state-of-the-art performance in identifying outliers compared to other recent methods, even with lower computation costs. Additionally, we show that our method yields robust performances when combined with privacy-preserving algorithms.
Unsupervised Anomaly Detection for Tabular Data Using Deep Noise Evaluation
Authors: Wei Dai; Kai Hwang; Jicong Fan
Venue: AAAI 2025 (main)

Abstract: Unsupervised anomaly detection (UAD) plays an important role in modern data analytics and it is crucial to provide simple yet effective and guaranteed UAD algorithms for real applications. In this paper, we present a novel UAD method for tabular data by evaluating how much noise is in the data. Specifically, we propose to learn a deep neural network from the clean (normal) training dataset and a noisy dataset, where the latter is generated by adding highly diverse noises to the clean data. The neural network can learn a reliable decision boundary between normal data and anomalous data when the diversity of the generated noisy data is sufficiently high so that the hard abnormal samples lie in the noisy region. Importantly, we provide theoretical guarantees, proving that the proposed method can detect anomalous data successfully, although the method does not utilize any real anomalous data in the training stage.
Extensive experiments through more than 60 benchmark datasets demonstrate the effectiveness of the proposed method in comparison to 12 baselines of UAD. Our method obtains a 92.27% AUC score and a 1.68 ranking score on average. Moreover, compared to the state-of-the-art UAD methods, our method is easier to implement.
Augmenting Sequential Recommendation with Balanced Relevance and Diversity
Authors: Yizhou Dang; Jiahui Zhang; Yuting Liu; Enneng Yang; Yuliang Liang; Guibing Guo; Jianzhe Zhao; Xingwei Wang
Venue: AAAI 2025 (main)

Abstract: By generating new yet effective data, data augmentation has become a promising method to mitigate the data sparsity problem in sequential recommendation. Existing works focus on augmenting the original data but rarely explore the issue of imbalanced relevance and diversity for augmented data, leading to semantic drift problems or limited performance improvements. In this paper, we propose a novel Balanced data Augmentation Plugin for Sequential Recommendation (BASRec) to generate data that balance relevance and diversity. BASRec consists of two modules: Single-sequence Augmentation and Cross-sequence Augmentation. The former leverages the randomness of the heuristic operators to generate diverse sequences for a single user, after which the diverse and the original sequences are fused at the representation level to obtain relevance. Further, we devise a reweighting strategy to enable the model to learn the preferences based on the two properties adaptively. The Cross-sequence Augmentation performs nonlinear mixing between different sequence representations from two directions. It produces virtual sequence representations that are diverse enough but retain the vital semantics of the original sequences. These two modules enhance the model to discover fine-grained preferences knowledge from single-user and cross-user perspectives. Extensive experiments verify the effectiveness of BASRec. The average improvement is up to 72.0% on GRU4Rec, 33.8% on SASRec, and 68.5% on FMLP-Rec. We demonstrate that BASRec generates data with a better balance between relevance and diversity than existing methods.
GeoAggregator: An Efficient Transformer Model for Geo-Spatial Tabular Data
Authors: Rui Deng; Ziqi Li; Mingshu Wang
Venue: AAAI 2025 (main)

Abstract: Modeling geospatial tabular data with deep learning has become a promising alternative to traditional statistical and machine learning approaches. However, existing deep learning models often face challenges related to scalability and flexibility as datasets grow. To this end, this paper introduces GeoAggregator, an efficient and lightweight algorithm based on transformer architecture designed specifically for geospatial tabular data modeling. GeoAggregators explicitly account for spatial autocorrelation and spatial heterogeneity through Gaussian-biased local attention and global positional awareness. Additionally, we introduce a new attention mechanism that uses the Cartesian product to manage the size of the model while maintaining strong expressive power. We benchmark GeoAggregator against spatial statistical models, XGBoost, and several state-of-the-art geospatial deep learning methods using both synthetic and empirical geospatial datasets. The results demonstrate that GeoAggregators achieve the best or second-best performance compared to their competitors on nearly all datasets. GeoAggregator's efficiency is underscored by its reduced model size, making it both scalable and lightweight. Moreover, ablation experiments offer insights into the effectiveness of the Gaussian bias and Cartesian attention mechanism, providing recommendations for further optimizing the GeoAggregator's performance.
Towards Global-Topology Relation Graph for Inductive Knowledge Graph Completion
Authors: Ling Ding; Lei Huang; Zhizhi Yu; Di Jin; Dongxiao He
Venue: AAAI 2025 (main)

Abstract: Knowledge Graphs (KGs) are structured data presented as directed graphs. Due to the common issues of incompleteness and inaccuracy encountered during construction and maintenance, completing KGs becomes a critical task. Inductive Knowledge Graph Completion (KGC) excels at inferring patterns or models from seen data to be applied to unseen data. However, existing methods mainly focus on new entities, while relations are usually randomly initialized. To this end, we propose TARGI, a simple yet effective inductive method for KGC. Specifically, we first construct a global relation graph for each topology from a global graph perspective, thus leveraging the in-variance of relation structures. We then utilize this graph to aggregate the rich embeddings of new relations and new entities, thereby performing KGC robustly in inductive scenarios. This successfully addresses the excessive reliance on the degree of relations and resolves the high complexity and limited scope of enclosing subgraph sampling in existing fully inductive algorithms. We conduct KGC experiments on six inductive datasets using inference data where entities are entirely new and new relations at 100 percent, 50 percent, and 0 percent radios. Extensive results demonstrate that our model accurately learns the topological structures and embeddings of new relations, and guides the embedding learning of new entities. Notably, our model outperforms 15 SOTA methods, especially in two fully inductive datasets.
On Finding Hubs in High Dimensions with Sampling
Authors: Huiwen Dong; Linghan Zeng; Zhiwen Zhao; Francesco Silvestri; Ninh Pham
Venue: AAAI 2025 (main)

Abstract: Hubs are a few points that frequently appear in the k-nearest neighbors (kNN) of many other points in a high-dimensional data set.
The hubs' effects, called the hubness phenomenon, degrade the performance of kNN based models in high dimensions.
We present SamHub, a simple sampling approach to efficiently identify hubs with theoretical guarantees. 
Apart from previous works based on approximate kNN indexes, SamHub is generic and applicable to any distance measure with negligible additional memory footprint. 
Empirically, by sampling only 10% of points, SamHub runs significantly faster and offers higher accuracy than existing hub detection methods on many real-world data sets with dot product, L1, L2, and dynamic time warping distances.
Our ablation studies of  SamHub on improving kNN-based classification show potential for other high-dimensional data analysis tasks.
Neural Reasoning for Sure Through Constructing Explainable Models
Authors: Tiansi Dong; Mateja Jamnik; Pietro Liò
Venue: AAAI 2025 (main)

Abstract: Neural networks remain black-box systems, unsure about their outputs, and their performance may drop unpredictably in real applications. An open question is how to qualitatively extend neural networks, so that they are sure about their reasoning results, or reasoning-for-sure. Here, we introduce set-theoretic relations explicitly and seamlessly into neural networks by extending vector embedding into sphere embedding, so that part-whole relations can explicitly encode set-theoretic relations through sphere boundaries in the vector space. A reasoning-for-sure neural network successfully constructs, within a constant number M of epochs, a sphere configuration as its semantic model for any consistent set-theoretic relation. We implement Hyperbolic Sphere Neural Network (HSphNN), the first reasoning-for-sure neural network for all types of Aristotelian syllogistic reasoning. Its construction process is realised as a sequence of neighbourhood transitions from the current towards the target configuration. We prove M=1 for HSphNN. In experiments, HSphNN achieves the symbolic level rigour of syllogistic reasoning and successfully checks both decisions and explanations of ChatGPT (gpt-3.5-turbo and gpt-4o) without errors. Through prompts, HSphNN improves the performance of gpt-3.5-turbo from 46.875% to 58.98%, and of gpt-4o from 82.42% to 84.76%. We show ways to extend HSphNN for various kinds of logical and Bayesian reasoning, and to integrate it with traditional neural networks seamlessly.
Active Large Language Model-Based Knowledge Distillation for Session-Based Recommendation
Authors: Yingpeng Du; Zhu Sun; Ziyan Wang; Haoyan Chua; Jie Zhang; Yew-Soon Ong
Venue: AAAI 2025 (main)

Abstract: Large language models (LLMs) provide a promising way for accurate session-based recommendation (SBR), but they demand substantial computational time and memory. Knowledge distillation (KD)-based methods can alleviate these issues by transferring the knowledge to a small student, which trains a student based on the predictions of a cumbersome teacher. However, these methods encounter difficulties for LLM-based KD in SBR. 1) It is expensive to make LLMs predict for all instances in KD. 2) LLMs may make ineffective predictions for some instances in KD, e.g., incorrect predictions for hard instances or similar predictions as existing recommenders for easy instances. In this paper, we propose an active LLM-based KD method in SBR, contributing to sustainable AI. To efficiently distill knowledge from LLMs with limited cost, we propose to extract a small proportion of instances predicted by LLMs. Meanwhile, for a more effective distillation, we propose an active learning strategy to extract instances that are as effective as possible for KD from a theoretical view. Specifically, we first formulate gains based on potential effects (e.g., effective, similar, and incorrect predictions by LLMs) and difficulties (e.g., easy or hard to fit) of instances for KD. Then, we propose to maximize the minimal gains of distillation to find the optimal selection policy for active learning, which can largely avoid extracting ineffective instances in KD. Experiments on real-world datasets show that our method significantly outperforms state-of-the-art methods for SBR.
Global Attribute-Association Pattern Aggregation for Graph Fraud Detection
Authors: Mingjiang Duan; Da He; Tongya Zheng; Lingxiang Jia; Mingli Song; Xinyu Wang; Zunlei Feng
Venue: AAAI 2025 (main)

Abstract: Fraud is increasingly prevalent, and its patterns are frequently changing, posing challenges for fraud detection methods such as random forests and Graph Neural Networks (GNNs), which rely on bin-based and mixture features separately. The former may lose crucial graph-associated features, while the latter face incorrect feature fusion. To overcome these limitations, we propose an approach based on attribute-association pattern that leverages the distinct attribute and association patterns differentiating fraudulent from benign behaviors, to enhance fraud detection capabilities. 
Attribute features are adaptively split into separate bins to eliminate incorrect attribute fusion and combine association patterns through graph neighbor message passing, thereby deriving attribute-association pattern features. Using the learned attribute-association patterns, the fraud patterns between a single pattern and the patterns across the entire graph are globally aggregated. Extensive experiments comparing our approach with 24 methods on 7 datasets demonstrate that the proposed method achieves SOTA performance.
Representation Space Augmentation for Effective Self-Supervised Learning on Tabular Data
Authors: Moonjung Eo; Kyungeun Lee; Hye-Seung Cho; Dongmin Kim; Ye Seul Sim; Woohyung Lim
Venue: AAAI 2025 (main)

Abstract: Tabular data, widely used across industries, remains underexplored in deep learning. Self-supervised learning (SSL) shows promise for pre-training deep neural networks (DNNs) on tabular data, but its potential is hindered by challenges in designing suitable augmentations. Unlike image and text data, where SSL leverages inherent spatial or semantic structures, tabular data lacks such explicit structure. This makes traditional input-level augmentations, like modifying or removing features, less effective due to difficulties in balancing critical information preservation with variability. To address these challenges, we propose RaTab, a novel method that shifts augmentation from input-level to representation-level using matrix factorization, specifically truncated SVD. This approach preserves essential data structures while generating diverse representations by applying dropout at various stages of the representation, thereby significantly enhancing SSL performance for tabular data.