OpenRaiser/Intern-Atlas huggingface dataset:
Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning
Authors: Yingqiang Ge, Xiaoting Zhao, Lucia Yu, Saurabh Paul, Diane Hu, Chu-Cheng Hsieh, Yongfeng Zhang
Venue: WSDM
Year: 2022

The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems (RS) touch and influence more and more people in their daily lives. In fairness-aware recommendation, most of the existing algorithmic approaches mainly aim at solving a constrained optimization problem by imposing a constraint on the level of fairness while optimizing the main recommendation objective, e.g., click through rate (CTR). While this alleviates the impact of unfair recommendations, the expected return of an approach may significantly compromise the recommendation accuracy due to the inherent trade-off between fairness and utility. This motivates us to deal with these conflicting objectives and explore the optimal trade-off between them in recommendation. One conspicuous approach is to seek aPareto efficient/optimal solution to guarantee optimal compromises between utility and fairness. Moreover, considering the needs of real-world e-commerce platforms, it would be more desirable if we can generalize the wholePareto Frontier, so that the decision-makers can specify any preference of one objective over another based on their current business needs. Therefore, in this work, we propose a fairness-aware recommendation framework usingmulti-objective reinforcement learning (MORL), called MoFIR (pronounced "more fair ''), which is able to learn a single parametric representation for optimal recommendation policies over the space of all possible preferences. Specially, we modify traditional Deep Deterministic Policy Gradient (DDPG) by introducingconditioned network (CN) into it, which conditions the networks directly on these preferences and outputs Q-value-vectors. Experiments on several real-world recommendation datasets verify the superiority of our framework on both fairness metrics and recommendation measures when compared with all other baselines. We also extract the approximate Pareto Frontier on real-world datasets generated by MoFIR and compare to state-of-the-art fairness methods.
Differentially Private Ensemble Classifiers for Data Streams
Authors: Lovedeep Gondara, Ke Wang, Ricardo Silva Carvalho
Venue: WSDM
Year: 2022

Learning from continuous data streams via classification/regression is prevalent in many domains. Adapting to evolving data characteristics (concept drift) while protecting data owners' private information is an open challenge. We present a differentially private ensemble solution to this problem with two distinguishing features: it allows anunbounded number of ensemble updates to deal with the potentially never-ending data streams under a fixed privacy budget, and it ismodel agnostic, in that it treats any pre-trained differentially private classification/regression model as a black-box. Our method outperforms competitors on real-world and simulated datasets for varying settings of privacy, concept drift, and data distribution.
Learning Multi-granularity Consecutive User Intent Unit for Session-based Recommendation
Authors: Jiayan Guo, Yaming Yang, Xiangchen Song, Yuan Zhang, Yujing Wang, Jing Bai, Yan Zhang
Venue: WSDM
Year: 2022

Session-based recommendation aims to predict a user's next action based on previous actions in the current session. The major challenge is to capture authentic and complete user preferences in the entire session. Recent work utilizes graph structure to represent the entire session and adopts Graph Neural Network (GNN) to encode session information. This modeling choice has been proved to be effective and achieved remarkable results. However, most of the existing studies only consider each item within the session independently and do not capture session semantics from a high-level perspective. Such limitation often leads to severe information loss and increases the difficulty of capturing long-range dependencies within a session. Intuitively, compared with individual items, a session snippet, i.e., a group of locally consecutive items, is able to provide supplemental user intents which are hardly captured by existing methods. In this work, we propose to learn multi-granularity consecutive user intent unit to improve the recommendation performance. Specifically, we creatively propose Multi-granularity Intent Heterogeneous Session Graph (MIHSG) which captures the interactions between different granularity intent units and relieves the burden of long-dependency. Moreover, we propose the Intent Fusion Ranking (IFR) module to compose the recommendation results from various granularity user intents. Compared with current methods that only leverage intents from individual items, IFR benefits from different granularity user intents to generate more accurate and comprehensive session representation, thus eventually boosting recommendation performance. We conduct extensive experiments on five session-based recommendation datasets and the results demonstrate the effectiveness of our method. Compared to current state-of-the-art methods, we achieve as large as 10.21% gain on HR@20 and 15.53% gain on MRR@20.
Multi-Scale Variational Graph AutoEncoder for Link Prediction
Authors: Zhihao Guo, Feng Wang, Kaixuan Yao, Jiye Liang, Zhiqiang Wang
Venue: WSDM
Year: 2022

Link prediction has become a significant research problem in deep learning, and the graph-based autoencoder model is one of the most important methods to solve it. The existing graph-based autoencoder models only learn a single set of distributions, which cannot accurately represent the mixed distribution in real graph data. Meanwhile, existing learning models have been greatly restricted when the graph data has insufficient attribute information and inaccurate topology information. In this paper, we propose a novel graph embedding framework, termed multi-scale variational graph autoencoder (MSVGAE), which learns multiple sets of low-dimensional vectors of different dimensions through the graph encoder to represent the mixed probability distribution of the original graph data, and performs multiple sampling in each dimension. Furthermore, a self-supervised learning strategy (i.e., graph feature reconstruction auxiliary learning) is introduced to fully use the graph attribute information to help the graph structure learning. Experiment studies on real-world graphs demonstrate that the proposed model achieves state-of-the-art performance compared with other baseline methods in link prediction tasks. Besides, the robustness analysis shows that the proposed MSVGAE method has obvious advantages in the processes of graph data with insufficient attribute information and inaccurate topology information.
Outside In: Market-aware Heterogeneous Graph Neural Network for Employee Turnover Prediction
Authors: Jinquan Hang, Zheng Dong, Hongke Zhao, Xin Song, Peng Wang, Hengshu Zhu
Venue: WSDM
Year: 2022

As an emerging initiative of proactive human resource management, employee turnover prediction is critically important for employers to retain talents and avoid the loss of intellectual capital. While considerable research efforts have been made in this direction, most of them only focus on modeling the within-company career trajectories of employees where the influence of external job market has been largely neglected. To this end, in this paper, we propose an enhanced framework of employee turnover prediction by jointly modeling the turnover clues from both internal and external views. Specifically, from the external-market view, we construct a heterogeneous graph which connects the employees with external job markets through shared skills. In this way, we can capture the potential popularity of employees in external markets specific to skills. Meanwhile, from the internal-company view, we design a graph convolutional network with hierarchical attention mechanism to capture the influence of organizational structure (e.g., superiors, subordinates, and peers) and colleagues with similar skills. Furthermore, both modules are modeled with Bidirectional LSTM and survival analysis to learn effective and dynamic representations of employee turnover prediction. Finally, we conduct extensive experiments on a large-scale real-world talent dataset with state-of-the-art methods, which clearly demonstrate the effectiveness of our approach as well as some interesting findings that could help us understand the employee turnover patterns, such as different impacts of external systems and collaborators from different groups.
Dy-HIEN: Dynamic Evolution based Deep Hierarchical Intention Network for Membership Prediction
Authors: Zhenyun Hao, Jianing Hao, Zhaohui Peng, Senzhang Wang, Philip S. Yu, Xue Wang, Jian Wang
Venue: WSDM
Year: 2022

Many video websites offer packages composed of paid videos. Users who purchase a package become members of the website, and thus can enjoy the membership service, such as watching the paid videos. It is practically important to predict which users will become members so that the website can recommend them the suitable packages for purchasing. Existing works generally predict the purchase behavior of users through capturing their interests in items. However, such works cannot be directly applied to the studied problem due to the following challenges. First, some important features of videos and packages change over time, such as the number of clicks and the update of the videos. Existing methods are not capable to capture such dynamic features. Second, a user's purchasing intention is very hard to capture. A user watching a video does not necessarily mean that he/she would like to purchase the corresponding package. In this paper, we propose a Dynamic Evolution based Deep Hierarchical Intention Network (Dy-HIEN for short) for membership prediction, which contains two modules. In the first module, we design a dynamic embedding learning method, applying multi-relational heterogeneous information network and attention mechanism to effectively represent the embedding of videos and packages. In the second module, a hierarchical method is proposed to extract the purchase intention of users. First, the video play history is divided into sessions based on the clicks on packages, and then time-order encoder and kernel functions are applied to mine the intention pattern associated with the package clicked in each session. Extensive experiments on real-world datasets are conducted to demonstrate the advantages of the proposed model on a variety of evaluation metrics.
POLE: Polarized Embedding for Signed Networks
Authors: Zexi Huang, Arlei Silva, Ambuj K. Singh
Venue: WSDM
Year: 2022

From the 2016 U.S. presidential election to the 2021 Capitol riots to the spread of misinformation related to COVID-19, many have blamed social media for today's deeply divided society. Recent advances in machine learning for signed networks hold the promise to guide small interventions with the goal of reducing polarization in social media. However, existing models are especially ineffective in predicting conflicts (or negative links) among users. This is due to a strong correlation between link signs and the network structure, where negative links between polarized communities are too sparse to be predicted even by state-of-the-art approaches. To address this problem, we first design a partition-agnostic polarization measure for signed graphs based on the signed random-walk and show that many real-world graphs are highly polarized. Then, we propose POLE (POLarized Embedding for signed networks), a signed embedding method for polarized graphs that captures both topological and signed similarities jointly via signed autocovariance. Through extensive experiments, we show that POLE significantly outperforms state-of-the-art methods in signed link prediction, particularly for negative links with gains of up to one order of magnitude.
AI & Public Data for Humanitarian and Emergency Response
Authors: Alejandro Jaimes
Venue: WSDM
Year: 2022

When an emergency event, or an incident relevant for peacekeeping or humanitarian needs first occurs, getting the right information as quickly as possible is critical in saving lives. When an event is ongoing, information on what is happening can be critical in making decisions to keep people safe and take control of the particular situation unfolding. In both cases, first responders, peacekeepers, and others have to quickly make decisions that include what resources to deploy and where. Fortunately, in most emergencies, people use social media to publicly share information. At the same time, sensor data is increasingly becoming available. But a platform to detect emergency situations and deliver the right information has to deal with ingesting thousands of noisy data points per second: sifting through and identifying relevant information, from different sources, in different formats, with varying levels of detail, in real time, so that relevant individuals and teams can be alerted at the right level and at the right time. In this talk I will describe the technical challenges in processing vast amounts of heterogenous, noisy data in real time from the web and other sources, highlighting the importance of interdisciplinary research and a human-centered approach to address problems in humanitarian and emergency response. I will give specific examples and discuss relevant future research directions in Machine Learning, NLP, Information Retrieval, Computer Vision and other fields, highlighting the role of knowledge combined with Neural and other approaches. This talk will present an overview, and draw from some of our publications at CVPR, AAAI, EMNLP, and others.
Triangle Graph Interest Network for Click-through Rate Prediction
Authors: Wensen Jiang, Yizhu Jiao, Qingqin Wang, Chuanming Liang, Lijie Guo, Yao Zhang, Zhijun Sun, Yun Xiong, Yangyong Zhu
Venue: WSDM
Year: 2022

Click-through rate prediction is a critical task in online advertising. Currently, many existing methods attempt to extract user potential interests from historical click behavior sequences. However, it is difficult to handle sparse user behaviors or broaden interest exploration. Recently, some researchers incorporate the item-item co-occurrence graph as an auxiliary. Due to the elusiveness of user interests, those works still fail to determine the real motivation of user click behaviors. Besides, those works are more biased towards popular or similar commodities. They lack an effective mechanism to break the diversity restrictions. In this paper, we point out two special properties of triangles in the item-item graphs for recommendation systems: Intra-triangle homophily and Inter-triangle heterophiy. Based on this, we propose a novel and effective framework named Triangle Graph Interest Network (TGIN). For each clicked item in user behavior sequences, we introduce the triangles in its neighborhood of the item-item graphs as a supplement. TGIN regards these triangles as the basic units of user interests, which provide the clues to capture the real motivation for a user clicking an item. We characterize every click behavior by aggregating the information of several interest units to alleviate the elusive motivation problem. The attention mechanism determines users' preference for different interest units. By selecting diverse and relative triangles, \short brings in novel and serendipitous items to expand exploration opportunities of user interests. Then, we aggregate the multi-level interests of historical behavior sequences to improve CTR prediction. Extensive experiments on both of public and industrial datasets clearly verify the effectiveness of our framework.
On Generalizing Static Node Embedding to Dynamic Settings
Authors: Di Jin, Sungchul Kim, Ryan A. Rossi, Danai Koutra
Venue: WSDM
Year: 2022

Temporal graph embedding has been widely studied thanks to its superiority in tasks such as prediction and recommendation. Despite the advances in algorithms and novel frameworks such as deep learning, there has been relatively little work on systematically studying the properties of temporal network models and their cornerstones, the graph time-series representations that are used in these approaches. This paper aims to fill this gap by introducing a general framework that extends an arbitrary existing static embedding approach to handle dynamic tasks, and conducting a systematic study of seven base static embedding methods and six temporal network models. Our framework generalizes static node embeddings derived from the time-series representation of stream data to the dynamic setting by modeling the temporal dependencies with classic models such as the reachability graph. While previous works on dynamic modeling and embedding have focused on representing a stream of timestamped edges using a time-series of graphs based on a specific time-scale (\eg, 1 month), we introduce the notion of an ε-graph time-series that uses a fixed number of edges for each graph, and show its superiority in practical settings over the standard solution. From the 42 methods that our framework subsumes, we find that leveraging the new ε-graph time-series representation and capturing temporal dependencies with the proposed reachability or summary graph tend to perform well. Furthermore, the new dynamic embedding methods based on our framework perform comparably and on average better than the state-of-the-art embedding methods designed specifically for temporal graphs in link prediction tasks.
KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification
Authors: Wei Ju, Junwei Yang, Meng Qu, Weiping Song, Jianhao Shen, Ming Zhang
Venue: WSDM
Year: 2022

This paper studies semi-supervised graph classification, which is an important problem with various applications in social network analysis and bioinformatics. This problem is typically solved by using graph neural networks (GNNs), which yet rely on a large number of labeled graphs for training and are unable to leverage unlabeled graphs. We address the limitations by proposing the Kernel-based Graph Neural Network (KGNN). A KGNN consists of a GNN-based network as well as a kernel-based network parameterized by a memory network. The GNN-based network performs classification through learning graph representations to implicitly capture the similarity between query graphs and labeled graphs, while the kernel-based network uses graph kernels to explicitly compare each query graph with all the labeled graphs stored in a memory for prediction. The two networks are motivated from complementary perspectives, and thus combing them allows KGNN to use labeled graphs more effectively. We jointly train the two networks by maximizing their agreement on unlabeled graphs via posterior regularization, so that the unlabeled graphs serve as a bridge to let both networks mutually enhance each other. Experiments on a range of well-known benchmark datasets demonstrate that KGNN achieves impressive performance over competitive baselines.
Leveraging World Events to Predict E-Commerce Consumer Demand under Anomaly
Authors: Dan Kalifa, Uriel Singer, Ido Guy, Guy D. Rosin, Kira Radinsky
Venue: WSDM
Year: 2022

Consumer demand forecasting is of high importance for many e-commerce applications, including supply chain optimization, advertisement placement, and delivery speed optimization. However, reliable time series sales forecasting for e-commerce is difficult, especially during periods with many anomalies, as can often happen during pandemics, abnormal weather, or sports events. Although many time series algorithms have been applied to the task, prediction during anomalies still remains a challenge. In this work, we hypothesize that leveraging external knowledge found in world events can help overcome the challenge of prediction under anomalies. We mine a large repository of 40 years of world events and their textual representations. Further, we present a novel methodology based on transformers to construct an embedding of a day based on the relations of the day's events. Those embeddings are then used to forecast future consumer behavior. We empirically evaluate the methods over a large e-commerce products sales dataset, extracted from eBay, one of the world's largest online marketplaces. We show over numerous categories that our method outperforms state-of-the-art baselines during anomalies.
GAGE: Geometry Preserving Attributed Graph Embeddings
Authors: Charilaos I. Kanatsoulis, Nicholas D. Sidiropoulos
Venue: WSDM
Year: 2022

Node embedding is the task of extracting concise and informative representations of certain entities that are connected in a network. Various real-world networks include information about both node connectivity and certain node attributes, in the form of features or time-series data. Modern representation learning techniques employ both the connectivity and attribute information of the nodes to produce embeddings in an unsupervised manner. In this context, deriving embeddings that preserve the geometry of the network and the attribute vectors would be highly desirable, as they would reflect both the topological neighborhood structure and proximity in feature space. While this is fairly straightforward to maintain when only observing the connectivity or attribute information of the network, preserving the geometry of both types of information is challenging. A novel tensor factorization approach for node embedding in attributed networks is proposed in this paper, that preserves the distances of both the connections and the attributes. Furthermore, an effective and lightweight algorithm is developed to tackle the learning task and judicious experiments with multiple state-of-the-art baselines suggest that the proposed algorithm offers significant performance improvements in downstream tasks.
Graph Neural Network Research at AWS AI
Authors: George Karypis
Venue: WSDM
Year: 2022

In the course of just a few years, Graph Neural Networks (GNNs) have emerged as the prominent supervised learning approach that brings the power of deep representation learning to graph and relational data. An ever-growing body of research has shown that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations. As a result, GNNs are quickly moving from the realm of academic research involving small graphs to powering commercial applications and very large graphs. This talk will provide an overview of some of the research that AWS AI has been doing to facilitate this transition, which includes developing the Deep Graph Library (DGL)-an open source framework for writing and training GNN-based models, improving the computational efficiency and scaling of GNN model training for extremely large graphs, developing novel GNN-based solutions for different applications, and making it easy for developers to train and use GNN models by integrating graph-based ML techniques in graph databases.
Query Interpretations from Entity-Linked Segmentations
Authors: Vaibhav Kasturia, Marcel Gohsen, Matthias Hagen
Venue: WSDM
Year: 2022

Web search queries can be ambiguous: is "source of the nile'' meant to find information on the actual river or on a board game of that name? We tackle this problem by deriving entity-based query interpretations: given some query, the task is to derive all reasonable ways of linking suitable parts of the query to semantically compatible entities in a background knowledge base. Our suggested approach focuses on effectiveness but also on efficiency since web search response times should not exceed some hundreds of milliseconds. In our approach, we use query segmentation as a pre-processing step that finds promising segment-based "interpretation skeletons''. The individual segments from these skeletons are then linked to entities from a knowledge base and the reasonable combinations are ranked in a final step. An experimental comparison on a combined corpus of all existing query entity linking datasets shows our approach to have a better interpretation accuracy at a better run time than the previously most effective methods.