Reviewerly/exHarmony huggingface dataset:
Satisfaction-Aware User Interest Network for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction plays a pivotal role in numerous industrial applications, including online advertising and recommender systems. Existing approaches primarily focus on modeling the correlation between user interests and candidate items. However, we argue that personalized user preferences for candidate items depend not only on correlation but also on the satisfaction of associated interests. To address this limitation, we propose SUIN, a novel CTR model that integrates satisfaction factors into user interest modeling for enhanced click-through rate prediction. Specifically, we employ a user interest satisfaction-aware network to capture the degree of satisfaction for each interest, thereby enabling adaptation of the user's personalized preference based on satisfaction levels. Additionally, we leverage the exposure-unclicked signal (recommended to the user but not clicked) as supervision during training, facilitating the interest satisfaction module to better model the satisfaction degree of user interests. Besides, this module serves as a foundational building block suitable for integration into mainstream sequential-based CTR models. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed model, outperforming state-of-the-art methods across various evaluation metrics. Furthermore, an online A/B test deployed on large-scale recommender systems shows significant improvements achieved by our model in diverse evaluation metrics.
Inducing Causal Structure for Abstractive Text Summarization

The mainstream of data-driven abstractive summarization models tends to explore the correlations rather than the causal relationships. Among such correlations, there can be spurious ones which suffer from the language prior learned from the training corpus and therefore undermine the overall effectiveness of the learned model. To tackle this issue, we introduce a Structural Causal Model (SCM) to induce the underlying causal structure of the summarization data. We assume several latent causal factors and non-causal factors, representing the content and style of the document and summary. Theoretically, we prove that the latent factors in our SCM can be identified by fitting the observed training data under certain conditions. On the basis of this, we propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq) to learn the causal representations that can mimic the causal factors, guiding us to pursue causal information for summary generation. The key idea is to reformulate the Variational Auto-encoder (VAE) to fit the joint distribution of the document and summary variables from the training corpus. Experimental results on two widely used text summarization datasets demonstrate the advantages of our approach.
Quantitative Decomposition of Prediction Errors Revealing Multi-Cause Impacts: An Insightful Framework for MLOps

As machine learning applications expand in various industries, MLOps, which enables continuous model operation and improvement, becomes increasingly significant. Identifying causes of prediction errors, such as low model performance or anomalous samples, and implementing appropriate countermeasures are essential for effective MLOps. Furthermore, quantitatively evaluating each cause's impact is necessary to determine the effectiveness of countermeasures. In this study, we propose a method to quantitatively decompose a single sample's prediction error into contributions from multiple causes. Our method involves four steps: calculating the prediction error, computing metrics related to error causes, using a regression model to learn the relationship between the error and metrics, and applying SHAP to interpret the model's predictions and calculate the contribution of each cause to the prediction error. Numerical experiments with open data show that our method offers valuable insights for model improvement, confirming the effectiveness of our approach.
FairGraph: Automated Graph Debiasing with Gradient Matching

As a prevalence data structure in the real world, graphs have found extensive applications ranging from modeling social networks to molecules. However, the existence of diverse biases within graphs gives rise to unfair representations learned by graph neural networks (GNNs). Addressing this issue has typically been approached from a modeling perspective, which not only compromises the integrity of the model structure but also entails additional effort and cost for retraining model parameters when the architecture changes. In this study, we adopt a data-centric standpoint to tackle the problem of fairness, focusing on graph debiasing for Graph Neural Networks. Our specific objective is to eliminate various biases from the input graph by generating a fair synthetic graph. By training GNNs on this fair graph, we aim to achieve an optimal accuracy-fairness trade-off. To this end, we propose FairGraph, which approaches the graph debiasing problem by mimicking the GNN training trajectory of the input graph through an optimization process involving a gradient-matching loss and fairness constraints. Through extensive experiments conducted on three benchmark datasets, we demonstrate the effectiveness of FairGraph and its ability to automatedly generate fair graphs that are transferable across different GNN architectures.
Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open Challenges

The proposed tutorial aims to familiarise the CIKM community with modern user profiling techniques that utilise Graph Neural Networks (GNNs). Initially, we will delve into the foundational principles of user profiling and GNNs, accompanied by an overview of relevant literature. We will subsequently systematically examine cutting-edge GNN architectures specifically developed for user profiling, highlighting the typical data utilised in this context. Furthermore, ethical considerations and beyond-accuracy perspectives, e.g. fairness and explainability, will be discussed regarding the potential applications of GNNs in user profiling. During the hands-on session, participants will gain practical insights into constructing and training recent GNN models for user profiling using open-source tools and publicly available datasets. The audience will actively explore the impact of these models through case studies focused on bias analysis and explanations of user profiles. To conclude the tutorial, we will analyse existing and emerging challenges in the field and discuss future research directions.
AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities

Multi-modal knowledge graphs (MMKGs) combine different modal data (e.g., text and image) for a comprehensive understanding of entities. Despite the recent progress of large-scale MMKGs, existing MMKGs neglect the multi-aspect nature of entities, limiting the ability to comprehend entities from various perspectives.In this paper, we construct AspectMMKG, the first MMKG with aspect-related images by matching images to different entity aspects. Specifically, we collect aspect-related images from a knowledge base, and further extract aspect-related sentences from the knowledge base as queries to retrieve a large number of aspect-related images via an online image search engine. Finally, AspectMMKG contains 2,380 entities, 18,139 entity aspects, and 645,383 aspect-related images. We demonstrate the usability of AspectMMKG in entity aspect linking (EAL) downstream task and show that previous EAL models achieve a new state-of-the-art performance with the help of AspectMMKG.To facilitate the research on aspect-related MMKG, we further propose an aspect-related image retrieval (AIR) model, that aims to correct and expand aspect-related images in AspectMMKG.We train an AIR model to learn the relationship between entity image and entity aspect-related images by incorporating entity image, aspect, and aspect image information. Experimental results indicate that the AIR model could retrieve suitable images for a given entity w.r.t different aspects.
NeoMaPy: A Parametric Framework for Reasoning with MAP Inference on Temporal Markov Logic Networks

Reasoning on inconsistent and uncertain data is challenging, especially for Knowledge-Graphs (KG) to abide temporal consistency. Our goal is to enhance inference with more general time interval semantics that specify their validity, as regularly found in historical sciences. We propose a new Temporal Markov Logic Networks (TMLN) model which extends the Markov Logic Networks (MLN) model with uncertain temporal facts and rules. Total and partial temporal (in)consistency relations between sets of temporal formulae are examined. We then propose a new Temporal Parametric Semantics (TPS) which allows combining several sub-functions leading to different assessment strategies. Finally, we present the new NeoMaPy tool, to compute the MAP inference on MLNs and TMLNs with several TPS. We compare our performances with state-of-the-art inference tools and exhibit faster and higher quality results.
Secure CodeCity: 3-dimensional visualization of software security facets

Over the last few decades, the software industry investigated security best practices to guide software developers in producing less vulnerable software products. As a result, security engineering has emerged as an integral part of the software development lifecycle. With the increase in the number of security vulnerabilities discovered, the software industry encountered challenges finding software security experts. Despite the availability of static code analysis tools to detect security vulnerabilities, they are underused due to several reasons such as inadequate usability and the lack of integration support. For example, such tools are deficient in providing enough information, produce faulty warning messages, and miscommunicate with developers. As a solution, this work presents a conceptual framework and a proof-of-concept visualization tool, Secure CodeCity, as an extension to the CodeCity metaphor, to facilitate security analytics. Secure CodeCity extends the CodeCity metaphor into three different granularity levels in 3-dimensional space, facilitating the vulnerability analysis in different granularities. Thus, software practitioners can use Secure CodeCity to obtain useful security-related information such as "What is the most vulnerable class/method in a particular software project?". A between-subjects design-based user study was conducted with 23 subjects using a set of security-related tasks with two benchmark open-source Apache projects. The evaluation results show that Secure CodeCity surpasses the state-of-the-art security analysis tools in terms of correctness, usability, and time efficiency.
Selecting Top-k Data Science Models by Example Dataset

Data analytical pipelines routinely involve various domain-specific data science models. Such models require expensive manual or training effort and often incur expensive validation costs (e.g., via scientific simulation analysis). Meanwhile, high-value models remain to be ad-hocly created, isolated, and underutilized for a broad community. Searching and accessing proper models for data analysis pipelines is desirable yet challenging for users without domain knowledge. This paper introduces ModsNet, a novel MODel SelectioN framework that only requires an Example daTaset. (1) We investigate the following problem: Given a library of pre-trained models, a limited amount of historical observations of their performance, and an "example" dataset as a query, return k models that are expected to perform the best over the query dataset. (2) We formulate a regression problem and introduce a knowledge-enhanced framework using a model-data interaction graph. Unlike traditional methods, (1) ModsNet uses a dynamic, cost-bounded "probe-and-select" strategy to incrementally identify promising pre-trained models in a strict cold-start scenario (when a new dataset without any interaction with existing models is given). (2) To reduce the learning cost, we develop a clustering-based sparsification strategy to prune unpromising models and their interactions. (3) We showcase of ModsNet built on top of a crowdsourced materials knowledge base platform. Our experiments verified its effectiveness, efficiency, and applications over real-world analytical pipelines.
Learning Visibility Attention Graph Representation for Time Series Forecasting

Visibility algorithm acts as a mapping that bridges graph representation learning with time series analysis, which has been broadly investigated for forecasting tasks. However, the intrinsic nature of visibility encoding yields graphs structured exclusively by binary adjacency matrix, leading to inevitable information loss of temporal sequence during the mapping. To this end, we introduce Angular Visibility Graph Networks (AVGNets), designed with two core features: (i) The framework reconstructs weighted graphs to encode time series by leveraging topological insights derived from visual angles of visibility networks, which capture sequential and structural information within weighted angular matrix. (ii) ProbAttention module is proposed for evaluating probabilistic attention of weighted networks, with remarkable capabilities to extract intrinsic and extrinsic temporal dependencies across multi-layer graphs. Extensive experiments and ablation studies on real-world datasets covering diverse ranges demonstrate that AVGNets achieve state-of-the-art performance, offering an innovative perspective on graph representation for sequence modeling.
SANN: Programming Code Representation Using Attention Neural Network with Optimized Subtree Extraction

Automated analysis of programming data using code representation methods offers valuable services for programmers, from code completion to clone detection to bug detection. Recent studies show the effectiveness of Abstract Syntax Trees (AST), pre-trained Transformer-based models, and graph-based embeddings in programming code representation. However, pre-trained large language models lack interpretability, while other embedding-based approaches struggle with extracting important information from large ASTs. This study proposes a novel Subtree-based Attention Neural Network (SANN) to address these gaps by integrating different components: an optimized sequential subtree extraction process using Genetic algorithm optimization, a two-way embedding approach, and an attention network. We investigate the effectiveness of SANN by applying it to two different tasks: program correctness prediction and algorithm detection on two educational datasets containing both small and large-scale code snippets written in Java and C, respectively. The experimental results show SANN's competitive performance against baseline models from the literature, including code2vec, ASTNN, TBCNN, CodeBERT, GPT-2, and MVG, regarding accurate predictive power. Finally, a case study is presented to show the interpretability of our model prediction and its application for an important human-centered computing application, student modeling. Our results indicate the effectiveness of the SANN model in capturing important syntactic and semantic information from students' code, allowing the construction of accurate student models, which serve as the foundation for generating adaptive instructional support such as individualized hints and feedback.
Proactive Streaming Analytics at Scale: A Journey from the State-of-the-art to a Production Platform

Proactive streaming analytics continuously extract real-time business value from massive data that stream in data centers or clouds. This requires (a) to process the data while they are still in motion; (b) to scale the processing to multiple machines, often over various, dispersed computer clusters, with diverse Big Data technologies; and (c) to forecast complex business events for proactive decision-making. Combining the necessary facilities for proactive streaming analytics at scale entails: (I) deep knowledge of the relevant state-of-the-art, (II) cherry-picking cutting edge research outcomes based on desired features and with the prospect of building interoperable components, and (III) building components and deploying them into a holistic architecture within a real-world platform. In this tutorial, we drive the audience through the whole journey from (I) to (III), delivering cutting edge research into a commercial analytics platform, for which we provide a hands-on experience.
Impact of Engaging with Intelligent Tutoring System Lessons Prior to Class Start

A study was conducted using the Generalized Intelligent Framework for Tutoring (GIFT) intelligent tutoring system (ITS) software and an Army Schoolhouse course. Existing materials were provided and used to create 9 computer-based GIFT lessons that included adaptive tutoring versions of the materials. These lessons included a Pre-Test, 7 content lessons (on the areas of Detect, Engage, and Assess), and a Post-Test. Six participants completed the Pre-Test, Post-Test, and all lessons. Overall Post-Test scores were significantly higher than Pre-Test scores, and remediation also had a positive impact on Post-Test scores. This study demonstrates that completing self-paced ITS lessons prior to initial class instruction can lead to positive learning impacts prior to the start of a class.
LEAD-ID: Language-Enhanced Denoising and Intent Distinguishing Graph Neural Network for Sponsored Search Broad Retrievals

As a local-based service (LBS), search ad retrieval in online meal delivery platforms should be broader to bridge the gap between vague consumption intentions of users and shortage of ad candidates limited by users' queries and positions. Recently, graph neural networks (GNNs) have been successfully applied to search ad retrieval task. However, directly applying GNNs suffer from noisy interactions and intents indistinguishability, which seriously degrades systems' effectiveness in the broad retrieval. In this paper, we propose a Language-EnhAnced Denoising and Intent Distinguishing graph neural network, LEAD-ID, which is developed and deployed at Meituan for sponsored search broad retrieval. To denoise interaction data, LEAD-ID designs hard- and soft- denoising strategies for GNNs based on a pretrained language model. A variational EM method is also employed to reduce high computational complexity of combining LMs and GNNs jointly. To distinguish various intents, LEAD-ID generates intent-aware node representations based on meticulously crafted LMs (language model) and GNNs; and then, it is guided by a contrastive learning object in an explicit and effective manner. According to offline experiments and online A/B tests, our framework significantly outperforms baselines in terms of recall and revenue.
Transferable Structure-based Adversarial Attack of Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNNs) have achieved remarkable development recently and exhibited superior performance in various tasks. However, recently HGNNs have been shown to have robustness weakness towards adversarial perturbations, which brings critical pitfalls for real applications, e.g. node classification and recommender systems. In particular, the transfer-based black-box attack is the most practical method to attack unknown models and poses a great threat to the reliability of HGNNs. In this work, we take the first step to explore the transferability of adversarial examples of HGNNs. Due to the overfitting of the source model, the adversarial perturbations generated by traditional methods usually exhibit unpromising transferability. To address this problem and boost adversarial transferability, we expect to seek common vulnerable directions of different models to attack. Inspired by the observation of the notable commonality of edge attention distribution between different HGNNs, we propose to guide the perturbation generation toward disrupting edge attention distribution. This edge attention-guided attack prioritizes the perturbation on edges that are more likely to be given common attention by different models, which benefits the transferability of adversarial perturbations. Finally, we develop two edge attention-guided attack methods towards heterogeneous relations tailored for HGNNs, called EA-FGSM and EA-PGD. Extensive experiments on six representative models and two datasets verify the effectiveness of our methods and form an unprecedented transfer robustness benchmark for HGNNs.
Self-supervised Learning and Graph Classification under Heterophily

Most existing pre-training strategies usually choose the popular Graph Neural Networks (GNNs), which can be seen as a special form of low-pass filter, but fail to effectively capture heterophily. In this paper, we first present an experimental investigation exploring the performance of low-pass and high-pass filters in heterophily graph classification, where the results clearly show that high-frequency signal is important for learning heterophily graph representation. In addition, it is still unclear how to effectively capture the structural pattern of graphs and how to measure the capability of the self-supervised pre-training strategy in capturing graph structure. To address the problem, we first design a quantitative Metric for Graph Structure (MGS), which analyzes the correlation between structural similarity and embedding similarity of graph pairs. Then, to enhance the graph structural information captured by self-supervised learning, we propose a novel self-supervised strategy for Pre-training GNNs based on the Metric (PGM). Extensive experiments validate our pre-training strategy achieves state-of-the-art performance for molecular property prediction and protein function prediction. In addition, we find choosing a suitable filter sometimes may be better than designing good pre-training strategies for heterophily graph classification.
Celebrity-aware Graph Contrastive Learning Framework for Social Recommendation

Social networks exhibit a distinct "celebrity effect" whereby influential individuals have a more significant impact on others compared to ordinary individuals, unlike other network structures such as citation networks and knowledge graphs. Despite its common occurrence in social networks, the celebrity effect is frequently overlooked by existing social recommendation methods when modeling social relationships, thereby hindering the full exploitation of social networks to mine similarities between users. In this paper, we fill this gap and propose a Celebrity-aware Graph Contrastive Learning Framework for Social Recommendation (CGCL), which explicitly models the celebrity effect in the social domain. Technically, we measure the different influences of celebrity and ordinary nodes by mining social network structure features, such as closeness centrality. To model the celebrity effect in social networks, we design a novel user-user impact-aware aggregation method, which incorporates the celebrity-aware influence information into the message propagation process. Additionally, we design a graph neural network-based framework which incorporates social semantics into the user-item interaction modeling with contrastive learning-enhanced data augmentation. The experimental results on three real-world datasets show the effectiveness of the proposed framework. We conduct ablation experiments to prove that the key components of our model benefit the recommendation performance improvement.