OpenRaiser/Intern-Atlas huggingface dataset:
ITA-ELECTION-2022: A Multi-Platform Dataset of Social Media Conversations Around the 2022 Italian General Election
Authors: Francesco Pierri, Geng Liu, Stefano Ceri
Venue: CIKM
Year: 2023

Online social media play a major role in shaping public discourse and opinion, especially during political events. We present the first public multi-platform dataset of Italian-language political conversations, focused on the 2022 Italian general election taking place on September 25th. Leveraging public APIs and a keyword-based search, we collected millions of posts published by users, pages and groups on Facebook, Instagram and Twitter, along with metadata of TikTok and YouTube videos shared on these platforms, over a period of four months. We augmented the dataset with a collection of political ads sponsored on Meta platforms, and a list of social media handles associated with political representatives. Our data resource will allow researchers and academics to further our understanding of the role of social media in the democratic process.
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange
Authors: Liang Zeng, Jin Xu, Zijun Yao, Yanqiao Zhu, Jian Li
Venue: CIKM
Year: 2023

Graph Neural Networks (GNNs) have already been widely used in various graph mining tasks. However, recent works reveal that the learned weights (channels) in well-trained GNNs are highly redundant, which inevitably limits the performance of GNNs. Instead of removing these redundant channels for efficiency consideration, we aim to reactivate them to enlarge the representation capacity of GNNs for effective graph learning. In this paper, we propose to substitute these redundant channels with other informative channels to achieve this goal. We introduce a novel GNN learning framework named AKE-GNN, which performs the Adaptive Knowledge Exchange strategy among multiple graph views generated by graph augmentations. AKE-GNN first trains multiple GNNs each corresponding to one graph view to obtain informative channels. Then, AKE-GNN iteratively exchanges redundant channels in the weight parameter matrix of one GNN with informative channels of another GNN in a layer-wise manner. Additionally, existing GNNs can be seamlessly incorporated into our framework. AKE-GNN achieves superior performance compared with various baselines across a suite of experiments on node classification, link prediction, and graph classification. In particular, we conduct a series of experiments on 15 public benchmark datasets, 8 popular GNN models, and 3 graph tasks and show that AKE-GNN consistently outperforms existing popular GNN models and even their ensembles. Extensive ablation studies and analyses on knowledge exchange methods validate the effectiveness of AKE-GNN.
Simplifying Temporal Heterogeneous Network for Continuous-Time Link prediction
Authors: Ce Li, Rongpei Hong, Xovee Xu, Goce Trajcevski, Fan Zhou
Venue: CIKM
Year: 2023

Temporal heterogeneous networks (THNs) investigate the structural interactions and their evolution over time in graphs with multiple types of nodes or edges. Existing THNs describe evolving networks as a sequence of graph snapshots and adopt mechanisms from static heterogeneous networks to capture the spatial-temporal correlation. However, these works are confined to the discrete-time setting and the implementation of stacked mechanisms often introduces a high level of complexity, both conceptually and computationally. Here, we conduct comprehensive examinations and propose STHN, a simplifying THN for continuous-time link prediction. Concretely, to integrate continuous dynamics, we maintain a historical interaction memory for each node. A link encoder that incorporates two components - type encoding and relative time encoding - is introduced to encapsulate implicit heterogeneous characteristics of interaction and extract the most informative temporal information. We further propose to use a patching technique that assists with Transformer feature extractor to support the interaction sequence with long histories. Extensive experiments on three real-world datasets empirically demonstrate that STHN outperforms state-of-the-art methods with competitive task accuracy and predictive efficiency on both transductive and inductive settings.
WOT-Class: Weakly Supervised Open-world Text Classification
Authors: Tianle Wang, Zihan Wang, Weitang Liu, Jingbo Shang
Venue: CIKM
Year: 2023

State-of-the-art weakly supervised text classification methods, while significantly reduced the required human supervision, still requires the supervision to cover all the classes of interest. This is never easy to meet in practice when human explore new, large corpora without complete pictures. In this paper, we work on a novel yet important problem of weakly supervised open-world text classification, where supervision is only needed for a few examples from a few known classes and the machine should handle both known and unknown classes in test time. General open-world classification has been studied mostly using image classification; however, existing methods typically assume the availability of sufficient known-class supervision and strong unknown-class prior knowledge (e.g., the number and/or data distribution). We propose a novel framework øur that lifts those strong assumptions. Specifically, it follows an iterative process of (a) clustering text to new classes, (b) mining and ranking indicative words for each class, and (c) merging redundant classes by using the overlapped indicative words as a bridge. Extensive experiments on 7 popular text classification datasets demonstrate that øur outperforms strong baselines consistently with a large margin, attaining 23.33% greater average absolute macro-F1 over existing approaches across all datasets. Such competent accuracy illuminates the practical potential of further reducing human effort for text classification.
DRL4IR: 4th Workshop on Deep Reinforcement Learning for Information Retrieval
Authors: Xin Xin, Xiangyu Zhao, Jin Huang, Weinan Zhang, Li Zhao, Dawei Yin, Grace Hui Yang
Venue: CIKM
Year: 2023

\AcIR is one of the most important fields to help users find relevant information. The interaction between IR systems and users can be naturally formulated as a decision-making problem. In the last decade, deep reinforcement learning (DRL) has become a promising direction to utilize the high model capacity of deep learning to improve long-term gains. On the one hand, there have been emerging research works focusing on leveraging DRL for IR tasks while the fundamental information theory under DRL settings, the principle of RL methods for IR tasks, or the experimental evaluation protocols of DRL-based IR systems, has not been deeply investigated. On the other hand, the emerging ChatGPT also provides new insights and challenges for DRL-based IR. Therefore, we propose the fourth DRL4IR workshop1 at CIKM 2023, which provides a venue for both academia researchers and industry practitioners to present the recent advances of DRL-based IR system, to foster novel research, interesting findings, and new applications of DRL for IR. We will pay more attention to fundamental research topics and recent application advances such as ChatGPT, with an expectation of over 300 workshop participants.
Forward Creation, Reverse Selection: Achieving Highly Pertinent Multimodal Responses in Dialogue Contexts
Authors: Ge Luo, Manman Zhang, Yuchen Ma, Sheng Li, Zhenxing Qian, Xinpeng Zhang
Venue: CIKM
Year: 2023

Multimodal Dialogue agents are often required to respond to conversation history using both textual and visual content. Even though current dialogue studies predominantly strive to generate natural texts or images, they fall short in considering the relevance of multimodal responses within a dialogue context, consequently confining agents from making prudent choices based on multiple alternatives and their associated relevance scores for decision-making. In this paper, we present a bidirectional multimodal dialogue framework that skillfully combines the forward generation of multiple text and image response candidates with reverse selection guided by relevance scores evaluated on dialogue context, facilitating agents in selecting the most suitable multimodal responses. Specifically, the forward generation aspect of our framework leverages a stage-wise approach, first producing textual replies and composite visual descriptions from the dialogue context, followed by the generation of visual responses aligned with the descriptions. In the reverse selection process, visual responses are translated into tangible descriptive texts that, in conjunction with textual responses, are inversely tied back to the dialogue context for relevance assessment, assigning a reference score to each multimodal response candidate to assist the intelligent agent in making informed decisions. Experimental outcomes demonstrate that our proposed bidirectional dialogue response framework markedly elevates performance in both automatic and human evaluations, yielding a range of contextually fitting multimodal responses for selection.
PopDCL: Popularity-aware Debiased Contrastive Loss for Collaborative Filtering
Authors: Zhuang Liu, Haoxuan Li, Guanming Chen, Yuanxin Ouyang, Wenge Rong, Zhang Xiong
Venue: CIKM
Year: 2023

Collaborative filtering (CF) is the basic method for recommendation with implicit feedback. Recently, various state-of-the-art CF integrates graph neural networks. However, they often suffer from popularity bias, causing recommendations to deviate from users' genuine preferences. Additionally, several contrastive learning methods based on the in-batch sample strategy have been proposed to train the CF model effectively, but they are prone to suffering from sample bias. To address this problem, debiased contrastive loss has been employed in the recommendation, but instead of personalized debiasing, it treats each user equally. In this paper, we propose a popularity-aware debiased contrastive loss for CF, which can adaptively correct the positive and negative scores based on the popularity of users and items. Our approach aims to reduce the negative impact of popularity and sample bias simultaneously. We theoretically analyze the effectiveness of the proposed method and reveal the relationship between popularity and gradient, which justifies the correction strategy. We extensively evaluate our method on three public benchmarks over balanced and imbalanced settings. The results demonstrate its superiority over the existing debiased strategies, not only on the entire datasets but also when segmenting the datasets based on item popularity.
A Two-tier Shared Embedding Method for Review-based Recommender Systems
Authors: Zhen Yang, Junrui Liu, Tong Li, Di Wu, Shiqiu Yang, Huan Liu
Venue: CIKM
Year: 2023

Reviews are valuable resources that have been widely researched and used to improve the quality of recommendation services. Recent methods use multiple full embedding layers to model various levels of individual preferences, increasing the risk of the data sparsity issue. Although it is a potential way to deal with this issue that models homophily among users who have similar behaviors, the existing approaches are implemented in a coarse-grained way. They calculate user similarities by considering the homophily in their global behaviors but ignore their local behaviors under a specific context. In this paper, we propose a two-tier shared embedding model (TSE), which fuses coarse- and fine-grained ways of modeling homophily. It considers global behaviors to model homophily in a coarse-grained way, and the high-level feature in the process of each user-item interaction to model homophily in a fine-grained way. TSE designs a whole-to-part principle-based process to fuse these ways in the review-based recommendation. Experiments on five real-world datasets demonstrate that TSE significantly outperforms state-of-the-art models. It outperforms the best baseline by 20.50% on the root-mean-square error (RMSE) and 23.96% on the mean absolute error (MAE), respectively. The source code is available at https://github.com/dianziliu/TSE.git.
Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation
Authors: Shuang Wang, Bahaeddin Eravci, Rustam Guliyev, Hakan Ferhatosmanoglu
Venue: CIKM
Year: 2023

Graph Neural Network (GNN) training and inference involve significant challenges of scalability with respect to both model sizes and number of layers, resulting in degradation of efficiency and accuracy for large and deep GNNs. We present an end-to-end solution that aims to address these challenges for efficient GNNs in resource constrained environments while avoiding the oversmoothing problem in deep GNNs. We introduce a quantization based approach for all stages of GNNs, from message passing in training to node classification, compressing the model and enabling efficient processing. The proposed GNN quantizer learns quantization ranges and reduces the model size with comparable accuracy even under low-bit quantization. To scale with the number of layers, we devise a message propagation mechanism in training that controls layer-wise changes of similarities between neighboring nodes. This objective is incorporated into a Lagrangian function with constraints and a differential multiplier method is utilized to iteratively find optimal embeddings. This mitigates oversmoothing and suppresses the quantization error to a bound. Significant improvements are demonstrated over state-of-the-art quantization methods and deep GNN approaches in both full-precision and quantized models. The proposed quantizer demonstrates superior performance in INT2 configurations across all stages of GNN, achieving a notable level of accuracy. In contrast, existing quantization approaches fail to generate satisfactory accuracy levels. Finally, the inference with INT2 and INT4 representations exhibits a speedup of 5.11 × and 4.70 × compared to full precision counterparts, respectively.
Assessing Student Performance with Multi-granularity Attention from Online Classroom Dialogue
Authors: Jiahao Chen, Zitao Liu, Shuyan Huang, Yaying Huang, Xiangyu Zhao, Boyu Gao, Weiqi Luo
Venue: CIKM
Year: 2023

Accurately judging students' ongoing performance is very crucial for real-world educational scenarios. In this work, we focus on the task of automatically predicting students' levels of mastery of math questions from teacher-student classroom dialogue data in the online learning environment. We propose a novel neural network armed with a multi-granularity attention mechanism to capture the personalized pedagogical instructions from the very noisy teacher-student dialogue transcriptions. We conduct experiments on a real-world educational dataset and the results demonstrate the superiority and availability of our model in terms of various evaluation metrics.
A Mix-up Strategy to Enhance Adversarial Training with Imbalanced Data
Authors: Wentao Wang, Harry Shomer, Yuxuan Wan, Yaxin Li, Jiangtao Huang, Hui Liu
Venue: CIKM
Year: 2023

Adversarial training has been proven to be one of the most effective techniques to defend against adversarial examples. The majority of existing adversarial training methods assume that every class in the training data is equally distributed. However, in reality, some classes often have a large number of training data while others only have a very limited amount. Recent studies have shown that the performance of adversarial training will degrade drastically if the training data is imbalanced. In this paper, we propose a simple yet effective framework to enhance the robustness of DNN models under imbalanced scenarios. Our framework, Imb-Mix, first augments the training dataset by generating multiple adversarial examples for samples in the minority classes. This is done by first adding random noise to the original adversarial examples created by one specific adversarial attack method. It then constructs Mixup-mimic mixed examples upon the augmented dataset used by adversarial training. In addition, we theoretically prove the regularization effect of our Mixup-mimic mixed examples generation technique in Imb-Mix. Extensive experiments on various imbalanced datasets verify the effectiveness of the proposed framework.
PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis
Authors: Heng Yang, Chen Zhang, Ke Li
Venue: CIKM
Year: 2023

The advancement of aspect-based sentiment analysis (ABSA) has highlighted the lack of a user-friendly framework that can significantly reduce the difficulty of reproducing state-of-the-art ABSA performance, especially for beginners. To meet this demand, we present PyABSA, a modularized framework built on PyTorch for reproducible ABSA. To facilitate ABSA research, PyABSA supports several ABSA subtasks, including aspect term extraction, aspect sentiment classification, and end-to-end aspect-based sentiment analysis. With just a few lines of code, the result of a model on a specific dataset can be reproduced. With a modularized design, PyABSA can also be flexibly extended to incorporate new models, datasets, and other related tasks. Additionally, PyABSA highlights its data augmentation and annotation features, which significantly address data scarcity. The project is available at: https://github.com/yangheng95/PyABSA.
Tutorial: Data Denoising Metrics in Recommender Systems
Authors: Pengfei Wang, Chenliang Li, Lixin Zou, Zhichao Feng, Kaiyuan Li, Xiaochen Li, Xialong Liu, Shangguang Wang
Venue: CIKM
Year: 2023

Recommender systems play a pivotal role in navigating users through vast reservoirs of information. However, data sparseness can compromise recommendation accuracy, making it challenging to improve recommendation performance. To address this issue, researchers have explored incorporating multiple data types. Yet, this approach can introduce noise that impairs the recommendations' accuracy. Therefore, it is crucial to denoise the data to enhance recommendation quality. This tutorial highlights the importance of data denoising metrics for improving the accuracy and quality of recommendations. Four groups of data denoising metrics are introduced: feature, item, pattern, and modality level. For each group, various denoising methods are presented. The tutorial emphasizes the significance of selecting the right data denoising methods to enhance recommendation quality. It provides valuable guidance for practitioners and researchers implementing reliable data denoising metrics in recommender systems. Finally, the tutorial proposes open research questions for future studies, making it a valuable resource for the research community.
The First Workshop on Personalized Generative AI @ CIKM 2023: Personalization Meets Large Language Models
Authors: Zheng Chen, Ziyan Jiang, Fan Yang, Zhankui He, Yupeng Hou, Eunah Cho, Julian J. McAuley, Aram Galstyan, Xiaohua Hu, Jie Yang
Venue: CIKM
Year: 2023

The First Workshop on Personalized Generative AI1 aims to be a cornerstone event fostering innovation and collaboration in the dynamic field of personalized AI. Leveraging the potent capabilities of Large Language Models (LLMs) to enhance user experiences with tailored responses and recommendations, the workshop is designed to address a range of pressing challenges including knowledge gap bridging, hallucination mitigation, and efficiency optimization in handling extensive user profiles. As a nexus for academics and industry professionals, the event promises rich discussions on a plethora of topics such as the development and fine-tuning of foundational models, strategies for multi-modal personalization, and the imperative ethical and privacy considerations in LLM deployment. Through a curated series of keynote speeches, insightful panel discussions, and hands-on sessions, the workshop aspires to be a catalyst in the development of more precise, contextually relevant, and user-centric AI systems. It aims to foster a landscape where generative AI systems are not only responsive but also anticipatory of individual user needs, marking a significant stride in personalized experiences.
Knowledge Graph Error Detection with Hierarchical Path Structure
Authors: Zhao Zhang, Fuwei Zhang, Fuzhen Zhuang, Yongjun Xu
Venue: CIKM
Year: 2023

Knowledge graphs (KGs) play a pivotal role in AI-related applications. In order to construct or continuously enrich KGs, automatic knowledge construction and update mechanisms are usually utilized, which inevitably bring in plenty of noise, and noise would degrade the performance of downstream applications. Existing KG error detection methods utilize the embeddings of entities and relations, or directly leverage the paths between entities to test the plausibility of triples, while ignore the valuable hierarchical information contained in the paths between entities. Indeed, the paths between a pair of entities conform to a hierarchical structure. Specifically, there may be a number of paths between two entities, and each path is comprised of several relations. The hierarchical structure is able to provide precious information, and is beneficial to leverage the path information in a fine-grained manner. To this end, in this paper, we propose a novel model named KG error detection with HiErarchical pAth stRucture (HEAR for short). Particularly, for a given triple, HEAR first learns path representations with the relations contained in the path, then integrates all path representations, and at last predicts the plausibility of the triple. Finally, we extensively validate the superiority of HEAR against various state-of-the-art baselines.
Incorporating Constituent Syntax into Grammatical Error Correction with Multi-Task Learning
Authors: Chen Chen, Bo He, Jing Yuan, Chunyan Hou, Xiaojie Yuan
Venue: CIKM
Year: 2023

Grammatical Error Correction (GEC) is usually considered as a translation task where an erroneous sentence is treated as the source language and the corrected sentence as the target language. The state-of-the-art GEC models often adopt transformer-based sequence-to-sequence architecture of machine translation. However, most of these approaches ignore the syntactic information because the syntax of an erroneous sentence is also full of errors and not beneficial to GEC. In this paper, we propose a novel Error-Correction Constituent Parsing (ECCP) task which uses the constituent parsing of corrected sentences to avoid the harmful effect of the erroneous sentence. We also propose an architecture that includes one encoder and two decoders. There are millions of parameters in transformer-based GEC models, and the labeled training data is substantially less than synthetic pre-training data. Therefore, adapter layers are added to the proposed architecture, and adapter tuning is used for fine-tuning our model to alleviate the low-resource issue. We conduct experiments on CoNLL-2014, BEA-2019, and JFLEG test datasets in unsupervised and supervised settings. Experimental results show that our method outperforms the-state-of-art baselines and achieves superior performance on all datasets.