OpenRaiser/Intern-Atlas huggingface dataset:
Beyond Aggregation: Efficient Federated Model Consolidation with Heterogeneity-Adaptive Weights Diffusion
Authors: Jiaqi Li, Xiaoyang Qu, Wenbo Ding, Zihao Zhao, Jianzong Wang
Venue: CIKM
Year: 2024

As the Internet of Things (IoT) evolves, the need for enhanced data-sharing to improve edge device performance has led to the adoption of Federated Learning (FL) for data privacy and optimized data utilization. However, communication costs in FL remain a significant challenge. Traditional methods focus on client enhancements but overlook server-side aggregation, potentially increasing client computation loads. In response, we introduce a novel method, FedDiff, which utilizes diffusion models for generating model weights on FL servers, replacing traditional aggregation methods. Our approach, tailored for heterogeneous environments, significantly improves communication efficiency, achieving faster convergence and robust performance against weight noise in rigorous tests.
MoTTo: Scalable Motif Counting with Time-aware Topology Constraint for Large-scale Temporal Graphs
Authors: Jiantao Li, Jianpeng Qi, Yueling Huang, Lei Cao, Yanwei Yu, Junyu Dong
Venue: CIKM
Year: 2024

Temporal motifs are recurring subgraph patterns in temporal graphs, and are present in various domains such as social networks, fraud detection, and biological networks. Despite their significance, counting temporal motifs efficiently remains a challenge, particularly on moderately sized datasets with millions of motif instances. To address this challenge, we propose a novel algorithm called Scalable Motif Counting with Time-aware Topology Constraint (MoTTo). MoTTo focuses on accurately counting temporal motifs with up to three nodes and three edges. It first utilizes a topology constraint-based pruning strategy to eliminate nodes that cannot participate in forming temporal motifs before the counting process. Then, it adopts a time-aware topology constraint-based pruning strategy to split large-scale datasets into independent partitions and filter out the unrelated ones, ensuring that the counting results remain unaffected. By investigating the second pruning strategy, we also find that MoTTo can be implemented in a multi-thread manner, further accelerating the counting process significantly. Experimental results on several real-world datasets of varying sizes demonstrate that MoTTo outperforms state-of-the-art methods in terms of efficiency, achieving up to a nine-fold improvement in total temporal motif counting. Specifically, the efficiency of counting triangular temporal motifs is enhanced by up to 31 times compared to state-of-the-art baselines.
Integrating Structure and Text for Enhancing Hyper-relational Knowledge Graph Representation via Structure Soft Prompt Tuning
Authors: Lijie Li, Hui Wang, Jiahang Li, Xiaodi Xu, Ye Wang, Tao Ren
Venue: CIKM
Year: 2024

Different from traditional knowledge graphs, where facts are usually represented as (subject, relation, object), hyper-relational knowledge graphs (HKGs) allow facts to be associated with additional relation-entity pairs to constrain the validity of facts. HKGs contain a substantial amount of textual information, which plays a crucial role in enriching representations. However, existing HKG embedding methods mainly rely on structural information but overlook textual information in HKGs, which are less effective in representing entities with limited structural information. To address this issue, the paper proposes HIST (Hyper-relational Knowledge Graph Encoder Integrating Structure and Text), which incorporates textual information and structural information in HKGs to enhance representations of entities and relations. HIST adopts the graph convolutional network to extract structural information and utilizes it to generate the Structure Soft Prompt. During the Structure Soft Prompt Tuning process, the textual information and structural information are fully integrated to generate more comprehensive representations. Additionally, an effective contrastive learning method for HKG embedding is formulated to improve the efficiency of negative sampling. Experimental results show that HIST achieves state-of-the-art performance on several public datasets. Our code is available at https://github.com/QieFangBaiLuQingYaJian/HIST.
Knowledge-enhanced Dynamic Modeling framework for Multi-Behavior Recommendation
Authors: Xiujuan Li, Nan Wang, Jin Zeng, Yingli Zhong, Zhonghui Shen
Venue: CIKM
Year: 2024

Multi-behavior recommendation (MBR) aims at predicting the items that the user will interact with at the next moment through the target behavior. Most existing MBR models are devoted to designing novel graph convolutional networks to combine multi-behavioral information. However, they ignore the negative impact of auxiliary behaviours, and also fail to take into account the effects of item characteristics. These limitations can lead to model performance degradation and affect user satisfaction. To address these issues, we propose a Knowledge-enhanced Dynamic Modeling framework for Multi-Behavior Recommendation (KDMBR). The algorithm utilises a multi-behavioral interaction module and a knowledge graph module to capture the user's overall interest and feature information respectively. The former designs a behavior-aware attention to distinguish contributions between behaviors. The latter introduces KG to enrich item characteristics and proposes a graph reconstruction strategy to enrich user information. Experiments on two large datasets further demonstrate the effectiveness of KDMBR.
Spectral and Geometric Spaces Representation Regularization for Multi-Modal Sequential Recommendation
Authors: Zihao Li, Xuekong Xu, Zuoli Tang, Lixin Zou, Qian Wang, Chenliang Li
Venue: CIKM
Year: 2024

Recent works demonstrate the effectiveness of multi-modal information for sequential recommendation. However, the computational cost and representation degeneration fail to be focused specifically and addressed adequately in multi-modality recommendation. To this end, we first identify and formalize three properties i.e., diversity, compactness, and consistency from the geometric space and spectrum perspective. Building upon this foundation, we devise tailored loss functions to regularize the above three properties for representation optimization. Theoretical underpinnings and experimental results demonstrate the efficacy of an enhanced item representation in ameliorating degeneration. Furthermore, we propose an efficient and expandable image-centered method, named E2 ImgRec, to mitigate the immense cost of computation. Concretely, we substitute the linear projection operations in the self-attention module and feed-forward network layer with two learnable rescaling vectors or efficient recommendation, then leverage cross-attention for multi-modality information fusion. Extensive experiments on three public datasets illustrate our method outperforms representative ID-based solutions and multi-modal based state-of-the-arts with only up to 39.9% in memory usage and 4.3× acceleration in training time. The code for replication is available at https://github.com/WHUIR/E2ImgRec.
Learning from Novel Knowledge: Continual Few-shot Knowledge Graph Completion
Authors: Zhuofeng Li, Haoxiang Zhang, Qiannan Zhang, Ziyi Kou, Shichao Pei
Venue: CIKM
Year: 2024

Knowledge graph (KG) completion has been increasingly recognized as a vital approach for uncovering missing knowledge and addressing the incompleteness issue in KGs. To enhance inference on rare relations and mitigate the impact of the long-tail distribution, the dominant strategy designs few-shot models following the meta-learning paradigm. However, these approaches typically operate under the assumption that KGs are available instantly, disregarding the newly emerging relations during KG enrichment. Thus, the emergence of these novel relations presents a need for few-shot models to continually learn from emerging knowledge. Although promising, two significant obstacles, i.e., catastrophic forgetting and the scarcity of novel relations, prevent effective learning from newly emerging relations. In this paper, we propose a novel framework designed to equip the few-shot model with the ability to learn sequentially from novel relations. Specifically, we introduce innovative strategies at both data and model levels: data-level rehearsal and model-level modulation to address catastrophic forgetting, alongside multi-view relation augmentation aimed at resolving the issue of insufficient novel relations. Extensive experiments conducted on real-world KGs validate the effectiveness of our proposed method.
Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation
Authors: Guojun Liang, Prayag Tiwari, Slawomir Nowaczyk, Stefan Byttner
Venue: CIKM
Year: 2024

Exploring the missing values is an essential but challenging issue due to the complex latent spatio-temporal correlation and dynamic nature of time series. Owing to the outstanding performance in dealing with structure learning potentials, Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) are often used to capture such complex spatio-temporal features in multivariate time series. However, these data-driven models often fail to capture the essential spatio-temporal relationships when significant signal corruption occurs. Additionally, calculating the high-order neighbor nodes in these models is of high computational complexity. To address these problems, we propose a novel higher-order spatio-temporal physics-incorporated GNN (HSPGNN). Firstly, the dynamic Laplacian matrix can be obtained by the spatial attention mechanism. Then, the generic inhomogeneous partial differential equation (PDE) of physical dynamic systems is used to construct the dynamic higher-order spatio-temporal GNN adaptively to obtain the missing time series values. Moreover, we estimate the missing impact by Normalizing Flows (NF) to evaluate the importance of each node in the graph for better explainability. Experimental results on four benchmark datasets demonstrate the effectiveness of HSPGNN and the superior performance when combining various order neighbor nodes. Also, graph-like optical flow, dynamic graphs, and missing impact can be obtained naturally by HSPGNN, which provides better dynamic analysis and explanation than traditional data-driven models.
ITIU: Intention Understanding via Interactive Table in Large Language Models
Authors: Zenghua Liao, Jinzhi Liao, Xiang Zhao
Venue: CIKM
Year: 2024

Large language models (LLMs) have shown impressive success in various applications. However, they encounter issues in accurately understanding user intentions, thereby impeding the successful accomplishment of tasks. The pioneering study tackles intention understanding through iteratively interacting with users to enhance response quality; however, it fails to identify the notorious challenges associated with the task, where efficiency and accuracy are paramount for ensuring optimal user experience. To address these challenges, we introduce a new interactive table based intention understanding (ITIU) framework, which refers to and implements non-linear thinking in psychology such that details of intention are parallelly generated. Specifically, in the table interacting design phase, ITIU first brainstorms a more concrete intention table relevant to user instructions and subsequently incorporates a rule-based supervision mechanism to enhance the accuracy of its content. In the specialized model training phase, we obtain the procedural records generated by ITIU to develop a specialized upstream interactive intention understanding model. The specialized model replaces internal steps within the original interaction design for further efficiency improvement. Comprehensive experimental results demonstrate that ITIU significantly outperforms existing intention understanding methods, particularly in terms of interaction efficiency and intention understanding accuracy. Furthermore, whether integrated into the open-source LLaMA or powerful LLMs like GPT-4 and Claude-3, ITIU shows significant performance improvements. All the data and codes are released.
Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP
Authors: Seonkyu Lim, Jeongwhan Choi, Noseong Park, Sang-Ha Yoon, ShinHyuck Kang, Young-Min Kim, Hyunjoong Kang
Venue: CIKM
Year: 2024

Gross domestic product (GDP) nowcasting is crucial for policy-making as GDP growth is a key indicator of economic conditions. Dynamic factor models (DFMs) have been widely adopted by government agencies for GDP nowcasting due to their ability to handle irregular or missing macroeconomic indicators and their interpretability. However, DFMs face two main challenges: i) the lack of capturing economic uncertainties such as sudden recessions or booms, and ii) the limitation of capturing irregular dynamics from mixed-frequency data. To address these challenges, we introduce NCDENow, a novel GDP nowcasting framework that integrates neural controlled differential equations (NCDEs) with DFMs. This integration effectively handles the dynamics of irregular time series.NCDENow consists of 3 main modules: i) factor extraction leveraging DFM, ii) dynamic modeling using NCDE, and iii) GDP growth prediction through regression. We evaluate NCDENow against 6 baselines on 2 real-world GDP datasets from South Korea and the United Kingdom, demonstrating its enhanced predictive capability. Our empirical results favor our method, highlighting the significant potential of integrating NCDE into nowcasting models. Our code and dataset are available at https://github.com/sklim84/NCDENow_CIKM2024.
Towards Robust Vision Transformer via Masked Adaptive Ensemble
Authors: Fudong Lin, Jiadong Lou, Xu Yuan, Nian-Feng Tzeng
Venue: CIKM
Year: 2024

Adversarial training (AT) can help improve the robustness of Vision Transformers (ViT) against adversarial attacks by intentionally injecting adversarial examples into the training data. However, this way of adversarial injection inevitably incurs standard accuracy degradation to some extent, thereby calling for a trade-off between standard accuracy and adversarial robustness. Besides, the prominent AT solutions are still vulnerable to adaptive attacks. To tackle such shortcomings, this paper proposes a novel ViT architecture, including a detector and a classifier bridged by our newly developed adaptive ensemble. Specifically, we empirically discover that detecting adversarial examples can benefit from the Guided Backpropagation technique. Driven by this discovery, a novel Multi-head Self-Attention (MSA) mechanism is introduced for enhancing our detector to sniff adversarial examples. Then, a classifier with two encoders is employed for extracting visual representations respectively from clean images and adversarial examples, with our adaptive ensemble to adaptively adjust the proportion of visual representations from the two encoders for accurate classification. This design enables our ViT architecture to achieve a better trade-off between standard accuracy and adversarial robustness. Besides, the adaptive ensemble technique allows us to mask off a random subset of image patches within input data, boosting our ViT's robustness against adaptive attacks, while maintaining high standard accuracy. Experimental results exhibit that our ViT architecture, on CIFAR-10, achieves the best standard accuracy and adversarial robustness of 90.3 % and 49.8 %, respectively.
GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation
Authors: Guojiao Lin, Zhen Meng, Dongjie Wang, Qingqing Long, Yuanchun Zhou, Meng Xiao
Venue: CIKM
Year: 2024

Multimodal recommendation systems (MMRS) have received considerable attention from the research community due to their ability to jointly utilize information from user behavior and product images and text. Previous research has two main issues. First, many long-tail items in recommendation systems have limited interaction data, making it difficult to learn comprehensive and informative representations. However, past MMRS studies have overlooked this issue. Secondly, users' modality preferences are crucial to their behavior. However, previous research has primarily focused on learning item modality representations, while user modality representations have remained relatively simplistic. To address these challenges, we propose a novel Graphs and User Modalities Enhancement (GUME) for long-tail multimodal recommendation. Specifically, we first enhance the user-item graph using multimodal similarity between items. This improves the connectivity of long-tail items and helps them learn high-quality representations through graph propagation. Then, we construct two types of user modalities: explicit interaction features and extended interest features. By using the user modality enhancement strategy to maximize mutual information between these two features, we improve the generalization ability of user modality representations. Additionally, we design an alignment strategy for modality data to remove noise from both internal and external perspectives. Extensive experiments on four publicly available datasets demonstrate the effectiveness of our approach. The code and data are publicly accessible via GitHub.
Enhancing Relevance of Embedding-based Retrieval at Walmart
Authors: Juexin Lin, Sachin Yadav, Feng Liu, Nicholas Rossi, Praveen Reddy Suram, Satya Chembolu, Prijith Chandran, Hrushikesh Mohapatra, Tony Lee, Alessandro Magnani, Ciya Liao
Venue: CIKM
Year: 2024

Embedding-based neural retrieval (EBR) is an effective search retrieval method in product search for tackling the vocabulary gap between customer search queries and products. The initial launch of our EBR system at Walmart yielded significant gains in relevance and add-to-cart rates [1]. However, despite EBR generally retrieving more relevant products for reranking, we have observed numerous instances of relevance degradation. Enhancing retrieval performance is crucial, as it directly influences product reranking and affects the customer shopping experience. Factors contributing to these degradations include false positives/negatives in the training data and the inability to handle query misspellings. To address these issues, we present several approaches to further strengthen the capabilities of our EBR model in terms of retrieval relevance. We introduce a Relevance Reward Model (RRM) based on human relevance feedback. We utilize RRM to remove noise from the training data and distill it into our EBR model through a multi-objective loss. In addition, we present the techniques to increase the performance of our EBR model, such as typo-aware training, and semi-positive generation. The effectiveness of our EBR is demonstrated through offline relevance evaluation, online AB tests, and successful deployments to live production.
Effective Job-market Mobility Prediction with Attentive Heterogeneous Knowledge Learning and Synergy
Authors: Sida Lin, Zhouyi Zhang, Yankai Chen, Chenhao Ma, Yixiang Fang, Shan Dai, Guangli Lu
Venue: CIKM
Year: 2024

Job-market mobility prediction plays a crucial role in optimizing human capital usage for both employees and employers. Most conventional methods primarily focus on learning sequential career sequences while ignoring the sufficient information extraction of mutual entity correlations in the job market. In this work, we push forward to exploit the heterogeneous relational knowledge among the job market structures by proposing a model namely Attentive Heterogeneous Knowledge Learning and Synergy (AHKLS). Equipped with the subsequent module of time-aware perception, AHKLS achieves effective career trajectory encoding for job-market mobility prediction. To evaluate the AHKLS performance, we conduct extensive experiments on three real-world datasets with different sizes. The empirical analyses demonstrate not only the performance superiority of AHKLS over several competing methods, but also the module effectiveness and model compatibility with other methods in enhancing the mobility prediction tasks accordingly.
Collaborative Cross-modal Fusion with Large Language Model for Recommendation
Authors: Zhongzhou Liu, Hao Zhang, Kuicai Dong, Yuan Fang
Venue: CIKM
Year: 2024

Despite the success of conventional collaborative filtering (CF) approaches for recommendation systems, they exhibit limitations in leveraging semantic knowledge within the textual attributes of users and items. Recent focus on the application of large language models for recommendation (LLM4Rec) has highlighted their capability for effective semantic knowledge capture. However, these methods often overlook the collaborative signals in user behaviors. Some simply instruct-tune a language model, while others directly inject the embeddings of a CF-based model, lacking a synergistic fusion of different modalities. To address these issues, we propose a framework of Collaborative Cross-modal Fusion with Large Language Models, termed CCF-LLM, for recommendation. In this framework, we translate the user-item interactions into a hybrid prompt to encode both semantic knowledge and collaborative signals, and then employ an attentive cross-modal fusion strategy to effectively fuse latent embeddings of both modalities. Extensive experiments demonstrate that CCF-LLM outperforms existing methods by effectively utilizing semantic and collaborative signals in the LLM4Rec context.
KMCT: k-Means Clustering of Trajectories Efficiently in Location-Based Services
Authors: Yuanjun Liu, Guanfeng Liu, Qingzhi Ma, Zhixu Li, Shiting Wen, Lei Zhao, An Liu
Venue: CIKM
Year: 2024

With the widespread use of GPS devices and the advancement of location-based services, a vast amount of trajectory data has been collected and mined for various applications. Trajectory clustering, which categorizes trajectories into distinct groups, is the fundamental functionality of trajectory data mining. The challenge is how to cluster on a mass of trajectory data efficiently and universally with satisfying results. The raw trajectory clustering algorithms are universal, but trapped in the dilemma between efficiency and desirable results. Other approaches, such as density-based, road network-based, and deep learning-based algorithms, encounter issues like high time complexity, loss of trajectory integrity, reliance on road networks, and data quality during training. To tackle these challenges, we first propose the efficient KMCT (k-Means Clustering of Trajectories) algorithm based on a semantic interpolation transformation to cluster raw trajectories and achieve satisfying results. Additionally, we introduce the DA-KMCT (Density Accelerated k-Means Clustering of Trajectories) algorithm to further boost the clustering process based on trajectory densities and an optimized centroid selecting strategy. Moreover, we present a novel clustering evaluation method called IOD, which efficiently estimates clustering results on large-scale datasets with linear time complexity. Experimental results on real-world datasets demonstrate that KMCT and DA-KMCT outperform five related methods in terms of clustering quality and time efficiency, and the proposed IOD evaluation shows a strong correlation with the Silhouette Coefficient, offering a reliable and efficient alternative for evaluating clustering results.