OpenRaiser/Intern-Atlas huggingface dataset:
2nd Workshop on Ethical Artificial Intelligence: Methods and Applications (EAI)
Authors: Chen Zhao 0010, Feng Chen 0001, Xintao Wu, Haifeng Chen, Jiayu Zhou
Venue: KDD
Year: 2023

Ethical AI has become increasingly important, and it has been attracting attention from academia and industry, due to its increased popularity in real-world applications with fairness concerns. It also places fundamental importance on ethical considerations in determining legitimate and illegitimate uses of AI. Organizations that apply ethical AI have clearly stated well-defined review processes to ensure adherence to legal guidelines. Therefore, the wave of research at the intersection of ethical AI in data mining and machine learning has also influenced other fields of science, including computer vision, natural language processing, reinforcement learning, and social science. Despite these successes, ethical AI still faces many challenges, such as a lack of interpretable and explainable methods for fairness-aware deep learning models, etc. Consequently, there is an urgent need to bring experts and researchers together at prestigious venues to discuss ethical AI, which has been rarely seen in previous KDD conferences. This workshop will provide a premium platform for both research and industry from different backgrounds to exchange ideas on opportunities, challenges, and cutting-edge techniques in ethical AI.
Improving Search Clarification with Structured Information Extracted from Search Results
Authors: Ziliang Zhao 0001, Zhicheng Dou, Yu Guo 0011, Zhao Cao, Xiaohua Cheng
Venue: KDD
Year: 2023

Search clarification in conversational search systems exhibits a clarification pane composed of several candidate aspect items and a clarifying question. To generate a pane, existing studies usually rely on unstructured document texts. However, important structured information in search results is not effectively considered, making the generated panes inaccurate in some cases. In this paper, we emphasize the importance of structured information in search results for improving search clarification. We propose enhancing unstructured documents with two kinds of structured information: one is "In-List'' relation obtained from HTML list structures, which helps extract groups of high-quality items with abundant parallel information. Another is "Is-A'' relation extracted from knowledge bases, which is helpful to generate good questions with explicit prompts. To avoid introducing excessive noises, we design a relation selection process to filter out ineffective relations. We further design a BART-based model for generating clarification panes. The experimental results show that the structured information is good supplement for generating high-quality clarification panes.
2nd Workshop on End-End Customer Journey Optimization
Authors: Hongying Zhao, Zhenyu Zhao, Anbang Xu, Neha Gupta, Mert Bay
Venue: KDD
Year: 2023

Nowadays, while most machine learning research on customer journey optimization has focused on short-term success metrics such as click-through rates and optimal ad placement, there has been little consideration given to developing a coherent system for end-to-end customer journey optimization. Such a system would encompass all aspects of the customer experience, from presenting the right product value to the right users, to understanding a user's likelihood of conversion and long-term value to the platform, as well as their propensity for cross-selling and risk of churning. Currently, models and algorithms for customer journey optimization are often developed in isolation, leading to inefficiencies in modeling and data pipelines. Furthermore, the customer is often viewed as a collection of different entities by different organizational departments (such as marketing, sales, and finance), which can lead to additional friction in the customer experience. This workshop seeks to bridge the gap between academic researchers and industrial practitioners who are interested in building holistic solutions for end-to-end customer journey optimization. By fostering collaboration and cross-disciplinary discussion, the workshop aims to accelerate progress in this rapidly evolving field.
Dense Representation Learning and Retrieval for Tabular Data Prediction
Authors: Lei Zheng, Ning Li 0029, Xianyu Chen, Quan Gan, Weinan Zhang 0001
Venue: KDD
Year: 2023

Data science is concerned with mining data patterns from a database, which is assembled by tabular data. As the routine of machine learning, most of the previous work mining the tabular data's pattern based on a single instance. However, they neglect the similar tabular data instances that could help make the label prediction of the target data instance. Recently, some retrieval-based methods for tabular data label prediction have been proposed, which, however, treat the data as sparse vectors to perform the retrieval, which fails to make use of the semantic information of the tabular data. To address such a problem, in this paper, we propose a novel framework of dense retrieval on tabular data (DERT) to support flexible data representation learning and effective label prediction on tabular data. DERT consists of two major components: (i) the encoder that makes the tabular data as embeddings, which could be trained by flexible neural networks and auxiliary loss functions; (ii) the retrieval and prediction component, which makes use of similar rows in the table to make label prediction of the target row. We test DERT on two tasks based on five real-world datasets and experimental results show that DERT achieves consistent improvements over the state-of-the-art and various baselines.
Road Planning for Slums via Deep Reinforcement Learning
Authors: Yu Zheng 0010, Hongyuan Su, Jingtao Ding, Depeng Jin, Yong Li 0008
Venue: KDD
Year: 2023

Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate road infrastructure within slums, and road planning for slums is critical to the sustainable development of cities. Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in terms of accessibility and construction costs. In this paper, we present a deep reinforcement learning based approach to automatically layout roads for slums. We propose a generic graph model to capture the topological structure of a slum, and devise a novel graph neural network to select locations for the planned roads. Through masked policy optimization, our model can generate road plans that connect places in a slum at minimal construction costs. Extensive experiments on real-world slums in different countries verify the effectiveness of our model, which can significantly improve accessibility by 14.3% against existing baseline methods. Further investigations on transferring across different tasks demonstrate that our model can master road planning skills in simple scenarios and adapt them to much more complicated ones, indicating the potential of applying our model in real-world slum upgrading. The code and data are available at https://github.com/tsinghua-fib-lab/road-planning-for-slums.
Online Few-Shot Time Series Classification for Aftershock Detection
Authors: Sheng Zhong 0005, Vinícius M. A. de Souza, Glenn Eli Baker, Abdullah Mueen
Venue: KDD
Year: 2023

Seismic monitoring systems sift through seismograms in real-time, searching for target events, such as underground explosions. In this monitoring system, a burst of aftershocks (minor earthquakes occur after a major earthquake over days or even years) can be a source of confounding signals. Such a burst of aftershock signals can overload the human analysts of the monitoring system. To alleviate this burden at the onset of a sequence of events (e.g., aftershocks), a human analyst can label the first few of these events and start an online classifier to filter out subsequent aftershock events. We propose an online few-shot classification model FewSig for time series data for the above use case. The framework of FewSig consists of a selective model to identify the high-confidence positive events which are used for updating the models and a general classifier to label the remaining events. Our specific technique uses a %two-level decision tree selective model based on sliding DTW distance and a general classifier model based on distance metric learning with Neighborhood Component Analysis (NCA). The algorithm demonstrates surprising robustness when tested on univariate datasets from the UEA/UCR archive. Furthermore, we show two real-world earthquake events where the FewSig reduces the human effort in monitoring applications by filtering out the aftershock events.
Automatic Temporal Relation in Multi-Task Learning
Authors: Menghui Zhou, Po Yang 0001
Venue: KDD
Year: 2023

Multi-task learning with temporal relation is a common prediction method for modelling the evolution of a wide range of systems. Considering the inherent relations between multiple time points, many works apply multi-task learning to jointly analyse all time points, with each time point corresponding to a prediction task. The most difficult challenge is determining how to fully explore and thus exploit the shared valuable temporal information between tasks to improve the generalization performance and robustness of the model. Existing works are classified as temporal smoothness and mean temporal relations. Both approaches, however, utilize a predefined and symmetric task relation structure that is too rigid and insufficient to adequately capture the intricate temporal relations between tasks. Instead, we propose a novel mechanism named Automatic Temporal Relation (AutoTR) for directly and automatically learning the temporal relation from any given dataset. To solve the biconvex objective function, we adopt the alternating optimization and show that the two related sub-optimization problems are amenable to closed-form computation of the proximal operator. To solve the two problems efficiently, the accelerated proximal gradient method is used, which has the fastest convergence rate of any first-order method. We have preprocessed six public real-life datasets and conducted extensive experiments to fully demonstrate the superiority of AutoTR. The results show that AutoTR outperforms several baseline methods on almost all datasets with different training ratios, in terms of overall model performance and every individual task performance. Furthermore, our findings verify that the temporal relation between tasks is asymmetrical, which has not been considered in previous works. The implementation source can be found at https://github.com/menghui-zhou/AutoTR.
Narrow the Input Mismatch in Deep Graph Neural Network Distillation
Authors: Qiqi Zhou 0003, Yanyan Shen, Lei Chen 0002
Venue: KDD
Year: 2023

Graph neural networks (GNNs) have been widely studied for modeling graph-structured data. Thanks to the over-parameterization and large receptive field of deep GNNs, "deep" is a promising direction to develop GNNs further and has shown some superior performances. However, the over-stacked structures of deep architectures incur high inference cost in deployment. To compress deep GNNs, we can use knowledge distillation (KD) to make shallow student GNNs mimic teacher GNNs. Existing KD methods in graph domain focus on constructing diverse supervision on embedding or prediction produced by student GNNs, but overlook the gap of the receptive field (i.e., input information) between student and teacher, which brings difficulties to KD. We call this gap "input mismatch". To alleviate this problem, we propose a lightweight stochastic extended module to provide an estimation for missing input information for student GNNs. The estimator models the distribution of missing information. Specifically, we model the missing information as an independent distribution from graph level and a conditional distribution from node level (given the condition of observable input). These two estimates are optimized using a Bayesian methodology and combined into a balanced estimate as additional input to student GNNs. To the best of our knowledge, we are the first to address the "input mismatch" problem in deep GNNs distillation. Experiments on extensive benchmarks demonstrate that our method outperforms existing KD methods for GNNs in distillation performance, which confirms that the estimations are reasonable and effective.
A Sublinear Time Algorithm for Opinion Optimization in Directed Social Networks via Edge Recommendation
Authors: Xiaotian Zhou, Liwang Zhu, Wei Li 0055, Zhongzhi Zhang
Venue: KDD
Year: 2023

In this paper, we study the opinion maximization problem for the leader-follower DeGroot model of opinion dynamics in a social network modelled by a directed graph with n nodes, where a small number of nodes are competing leader nodes with binary opposing opinions 0 or 1, and the rest are follower nodes. We address the problem of maximizing the overall opinion by adding k ⇐ n new edges, where each edge is incident to a 1-leader and a follower. We prove that the objective function is monotone and submodular, and then propose a deterministic greedy algorithm with an approximation ratio (1-1 over e) and O(n3) running time. We then develop a fast sampling algorithm based on l-truncated absorbing random walks and sample-materialization techniques, which has sublinear time complexity O(kn1/2 log3/2 n/ε3) for any error parameter ε > 0. We provide extensive experiments on real networks to evaluate the performance of our algorithms. The results show that for undirected graphs our fast sampling algorithm outperforms the state-of-the-art method in terms of efficiency and effectiveness. While for directed graphs our fast sampling algorithm is as effective as our deterministic greedy algorithm, both of which are much better than the baseline strategies. Moreover, our fast algorithm is scalable to large directed graphs with over 41 million nodes.
C-AOI: Contour-based Instance Segmentation for High-Quality Areas-of-Interest in Online Food Delivery Platform
Authors: Yida Zhu, Liying Chen, Daping Xiong, Shuiping Chen, Fangxiao Du, Jinghua Hao, Renqing He, Zhizhao Sun
Venue: KDD
Year: 2023

Online food delivery (OFD) services have become popular globally, serving people's daily needs. Precise area-of-interest (AOI) boundaries help OFD platforms determine customers' exact locations, which is crucial for maintaining consistency in delivery difficulty and providing a uniform customer experience within an AOI. Existing AOI generation methods primarily rely on predefined shapes or density-based clustering, which limits the quality of the contours. Recently, Meituan has treated the AOI contours as a binary semantic segmentation problem. Their approach involves a multi-step post-process to address the issues with boundary breaks caused by semantic segmentation models, leading to decreased quality and inefficiency in the learning process. In this paper, we propose a novel method for AOI contour generation called C-AOI (Contour-based Area-of-Interest). C-AOI is an instance segmentation model that focuses on generating high-quality AOI contours. Unlike the former method, which relies on pixel-by-pixel classification, C-AOI starts from the center point of the AOI and regresses the boundary. This approach results in a higher-quality boundary and is less computationally intensive. C-AOI first corrects errors on the contour using a local aggregation mechanism. Then, we propose a novel deforming module called the contour transformer, which captures the global geometry of the object. To enhance the positional relationship among vertices, we introduce a learnable cyclic positional encoding applied to the contour transformer. Finally, to improve the boundary details, we propose the Adaptive Matching Loss (AML) that eliminates over-smoothed boundaries and promotes optimized convergence pathways. Experimental results on real-world datasets collected from Meituan have demonstrated that C-AOI significantly improves the mask and boundary quality compared to Meituan's previous work. Moreover, Its inference speed is comparable to that of E2EC, a state-of-the-art real-time contour-based method. It is noteworthy that C-AOI has been deployed in the Meituan platform for producing AOIs.
WinGNN: Dynamic Graph Neural Networks with Random Gradient Aggregation Window
Authors: Yifan Zhu 0001, Fangpeng Cong, Dan Zhang, Wenwen Gong, Qika Lin, Wenzheng Feng, Yuxiao Dong, Jie Tang 0001
Venue: KDD
Year: 2023

Modeling the dynamics into graph neural networks (GNNs) contributes to the understanding of evolution in dynamic graphs, which helps optimize temporal-spatial representations for real-world dynamic network problems. Empirically, dynamic GNN embedding requires additional temporal encoders, which inevitably introduces additional learning parameters to make dynamic GNNs oversized and inefficient. Furthermore, previous dynamic GNN models are under the same fixed temporal term, which causes the short-temporal optimum. To address these issues, we propose the WinGNN framework to model dynamic graphs, which is realized by a simple GNN model with the meta-learning strategy and a novel mechanism of random gradient aggregation. WinGNN calculates the frame-wise loss of the current snapshot and passes the loss gradient to the next to model graph dynamics without temporal encoders. Then it introduces the randomized sliding-window to acquire the window-aware gradienton consecutive snapshots, and the calculated two types of gradient are aggregated to update the GNN, thereby reducing the parameter size and improving the robustness. Experiments on six public datasets show the advantage of our WinGNN compared with existing baselines, where it has reached the optimum in twenty-two out of twenty-four performance metrics.
A Feature-Based Coalition Game Framework with Privileged Knowledge Transfer for User-tag Profile Modeling
Authors: Xianghui Zhu, Peng Du, Shuo Shao 0001, Chenxu Zhu, Weinan Zhang 0001, Yang Wang 0019, Yang Cao
Venue: KDD
Year: 2023

User-tag profiling is an effective way of mining user attributes in modern recommender systems. However, prior researches fail to extract users' precise preferences for tags in the items due to their incomplete feature-input patterns. To convert user-item interactions to user-tag preferences, we propose a novel feature-based framework named Coalition Tag Multi-View Mapping (CTMVM), which identifies and investigates two special features, Coalition Feature and Privileged Feature. The former indicates decisive tags in each click where relationships between tags in one item are treated as a coalition game. The latter represents highly informative features that only occur during training. For the coalition feature, we adopt Shapley Value based Empowerment (SVE) to model the tags in items with a game-theoretic paradigm and charge the network to straight master user preferences for essential tags. For the privileged feature, we present Privileged Knowledge Mapping (PKM) to explicitly distill privileged feature knowledge for each tag into one single embedding, which assists the model in predicting user-tag preferences at a more fine-grained level. However, the barren capacity of single embeddings limits the diverse relations between each tag and different privileged features. Therefore, we further propose Adaptive Multi-View Mapping (AMVM) model to enhance effect by handling multiple mapping networks. Excellent offline experiment results on two public and one private datasets show the out-standing performance of CTMVM. After the deployment on Alibaba large-scale recommendation systems, CTMVM achieved improvement by 10.81% and 6.74% in terms of Theme-CTR and Item-CTR respectively, which validates the effectiveness of taking in the two particular features for training.
Path-Specific Counterfactual Fairness for Recommender Systems
Authors: Yaochen Zhu, Jing Ma 0002, Liang Wu 0006, Qi Guo, Liangjie Hong, Jundong Li
Venue: KDD
Year: 2023

Recommender systems (RSs) have become an indispensable part of online platforms. With the growing concerns of algorithmic fairness, RSs are not only expected to deliver high-quality personalized content, but are also demanded not to discriminate against users based on their demographic information. However, existing RSs could capture undesirable correlations between sensitive features and observed user behaviors, leading to biased recommendations. Most fair RSs tackle this problem by completely blocking the influences of sensitive features on recommendations. But since sensitive features may also affect user interests in a fair manner (e.g., race on culture-based preferences), indiscriminately eliminating all the influences of sensitive features inevitably degenerate the recommendations quality and necessary diversities. To address this challenge, we propose a path-specific fair RS (PSF-RS) for recommendations. Specifically, we summarize all fair and unfair correlations between sensitive features and observed ratings into two latent proxy mediators, where the concept of path-specific bias (PS-Bias) is defined based on path-specific counterfactual inference. Inspired by Pearl's minimal change principle, we address the PS-Bias by minimally transforming the biased factual world into a hypothetically fair world, where a fair RS model can be learned accordingly by solving a constrained optimization problem. For the technical part, we propose a feasible implementation of PSF-RS, i.e., PSF-VAE, with weakly-supervised variational inference, which robustly infers the latent mediators such that unfairness can be mitigated while necessary recommendation diversities can be maximally preserved simultaneously. Experiments conducted on semi-simulated and real-world datasets demonstrate the effectiveness of PSF-RS.
The 4th International Workshop on Talent and Management Computing (TMC'2023)
Authors: Hengshu Zhu, Hui Xiong 0001, Yong Ge, Ee-Peng Lim
Venue: KDD
Year: 2023

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with the talent and management related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for business leaders to understand the rules of talent and management, which in turn deliver intelligence for effective decision making and management for their organizations. In the past few years, talent and management computing have increasingly attracted attentions from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 4th International Workshop on Talent and Management Computing (TMC'2023), is to bring together researchers and practitioners to discuss both the critical problems faced by talent and management related domains, and potential data-driven solutions by leveraging state-of-the-art data mining technologies.