OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Sentence Argument Linking via An Event-Aware Hierarchical Encoder
Authors: Hang Yang, Yubo Chen, Kang Liu, Jun Zhao, Taifeng Wang
Venue: CIKM
Year: 2021

Multi-sentence argument linking aims at detecting implicit event arguments across sentences, which is indispensable when textual events span across multiple sentences in a document. Previous studies suffer from the inherent limitations of error propagation and lack the explicit modeling of the local and non-local interactions in a textual event. In this paper, we propose an event-aware hierarchical encoder for multi-sentence argument linking. Specifically, we introduce a hierarchical encoder to explicitly capture the local and global interactions in a textual event. Furthermore, we introduce an auxiliary task to predict the event-relevant context in a manner of multi-task learning, which can implicitly benefit the argument linking model to be aware of the event-relevant context. The empirical results on the widely used argument linking dataset show that our model significantly outperforms the baselines, which demonstrates the effectiveness of our proposed method.
Adversarial Kernel Sampling on Class-imbalanced Data Streams
Authors: Peng Yang, Ping Li
Venue: CIKM
Year: 2021

This paper investigates online active learning in the setting of class-imbalanced data streams, where labels are allowed to be queried of with limited budgets. In this setup, conventional learning would be biased towards majority classes and consequently harm the performance. To address this issue, imbalance learning technique adopts both asymmetric losses and asymmetric queries to tackle the imbalance. Although this approach is effective, it may not guarantee the performance in an adversarial setting where the actual labels are unknown, and they may be chosen by the adversary To learn a promising hypothesis in class-imbalanced and adversarial environment, we propose an asymmetric min-max optimization framework for online classification. The derived algorithm can track the imbalance and bound the choices of an adversary simultaneously. Despite the promising result, this algorithm assumes that the label is provided for every input, while label is scare and labeling is expensive in real-world application. To this end, we design a confidence-based sampling strategy to query the informative labels within a budget. We theoretically analyze this algorithm in terms of mistake bound, and two asymmetric measures. Empirically, we evaluate the algorithms on multiple real-world imbalanced tasks. Promising results could be achieved on various application domains.
Node2Grids: A Cost-Efficient Uncoupled Training Framework for Large-Scale Graph Learning
Authors: Dalong Yang, Chuan Chen, Youhao Zheng, Zibin Zheng, Shih-wei Liao
Venue: CIKM
Year: 2021

Graph Convolutional Network (GCN) has been widely used in graph learning tasks. However, GCN-based models (GCNs) are inherently coupled training frameworks repetitively conducting the recursive neighborhood aggregation, which leads to high computational and memory overheads when processing large-scale graphs. To tackle these issues, we present Node2Grids, a cost-efficient uncoupled training framework that leverages the independent mapped data for obtaining the embedding. Instead of directly processing the coupled nodes as GCNs, Node2Grids supports a more efficacious method in practice, mapping the coupled graph data into the independent grid-like data which can be fed into the uncoupled models as Convolutional Neural Network (CNN). This simple but valid strategy significantly saves memory and computational resources while achieving comparable results with the leading GCN-based models. Specifically, in order to support a general and convenient mapping approach, Node2Grids selects the most influential neighborhood with central node fusion information to construct the grid-like data. To further improve the downstream tasks' efficiency, a simple CNN-based neural network is employed to capture the significant information from the mapped grid-like data. Moreover, the grid-level attention mechanism is implemented, which enables implicitly specifying the different weights for the extracted grids of CNN. In addition to the typical transductive and inductive learning tasks, we also verify our framework on million-scale graphs to demonstrate the superiority of cost performance against the state-of-the-art GCN-based approaches. The codes are available on the GitHub link.
Seasonal Relevance in E-Commerce Search
Authors: Haode Yang, Parth Gupta, Roberto Fernández Galán, Dan Bu, Dongmei Jia
Venue: CIKM
Year: 2021

Seasonality is an important dimension for relevance in e-commerce search. For example, a query jacket has a different set of relevant documents in winter than summer. For an optimal user experience, the e-commerce search engines should incorporate seasonality in product search. In this paper, we formally introduce the concept of seasonal relevance, define it and quantify using data from a major e-commerce store. In our analyses, we find 39% queries are highly seasonally relevant to the time of search and would benefit from handling seasonality in ranking. We propose LogSR and VelSR features to capture product seasonality using state-of-the-art neural models based on self-attention. Comprehensive offline and online experiments over large datasets show the efficacy of our methods to model seasonal relevance. The online A/B test on 784 MM queries shows the treatment with seasonal relevance features results in 2.20% higher purchases and better customer experience overall.
Knowledge Graph Representation Learning as Groupoid: Unifying TransE, RotatE, QuatE, ComplEx
Authors: Han Yang, Junfei Liu
Venue: CIKM
Year: 2021

Knowledge graph (KG) representation learning which aims to encode entities and relations into low-dimensional spaces, has been widely used in KG completion and link prediction. Although existing KG representation learning models have shown promising performance, the theoretical mechanism behind existing models is much less well-understood. It is challenging to accurately portray the internal connections between models and build a competitive model systematically. To overcome this problem, a unified KG representation learning framework, called GrpKG, is proposed in this paper to model the KG representation learning from a generic groupoid perspective. We discover that many existing models are essentially the same in the sense of groupoid isomorphism and further provide transformation methods between different models. Moreover, we explore the applications of GrpKG in the model classification as well as other processes. The experiments on several benchmark data sets validate the effectiveness and superiority of our framework by comparing two proposed models (GrpQ8 and GrpM2) with the state-of-the-art models.
On the Diversity and Explainability of Recommender Systems: A Practical Framework for Enterprise App Recommendation
Authors: Wenzhuo Yang, Jia Li, Chenxi Li, Latrice Barnett, Markus Anderle, Simo Arajärvi, Harshavardhan Utharavalli, Caiming Xiong, Steven C. H. Hoi
Venue: CIKM
Year: 2021

This paper introduces an enterprise app recommendation problem with a new "to-business'' use case, which aims to assist a sales team acting as the bridge connecting the applications and developers with the customers who apply these apps to solve their business problems. Our recommender system is an assistant to the sales team, helping recommend relevant apps to the customers for their businesses and increasing the likelihood of improving sales revenue. Besides recommendation accuracy, recommendation diversity and explainability are even more crucial since they provide more exposure opportunities for app developers and improve the transparency and trustworthiness of the recommender system. To allow the sales team to explore unpopular but relevant apps and understand why such apps are recommended, we propose a novel framework for improving aggregate recommendation diversity and generating recommendation explanations, which supports a wide variety of models for improving recommendation accuracy. The model in our framework is simple yet effective, which can be trained in an end-to-end manner and deployed as a recommendation service easily. Furthermore, our framework can also apply to other generic recommender systems for improving diversity and generating explanations. Experiments on public and private datasets demonstrate the effectiveness of our framework and solution.
CIExplore: Curiosity and Influence-based Exploration in Multi-Agent Cooperative Scenarios with Sparse Rewards
Authors: Huanhuan Yang, Dianxi Shi, Chenran Zhao, Guojun Xie, Shaowu Yang
Venue: CIKM
Year: 2021

Learning in a sparse-reward setting is a well-known challenge in RL (Reinforcement Learning). In the single-agent domain, this challenge can be addressed by introducing exploration bonuses driven by intrinsic motivation to encourage agents to visit unseen states. However, naively applying these methods in MARL (Multi-Agent Reinforcement Learning) cooperative settings with sparse rewards results in some inevitable problems: misunderstanding environmental knowledge and lack of collaboration among agents, etc. Based on this, in this paper, we propose the Curiosity and Influence-based Explore (CIExplore) method, which includes a new form of intrinsic reward and an internal counterfactual advantage function. Concretely, the intrinsic reward is a combination of joint curiosity reward and influence reward. The former is the variance of outputs across an ensemble of prediction models that take joint observations and actions of all agents as inputs to predict the next time's joint observations. And the latter quantifies the influence of one agent's behavior on other agents' state-value functions. Given that the joint curiosity reward is shared by all agents, we compute an internal counterfactual advantage function to address this intrinsic reward assignment problem. We demonstrate the efficacy of CIExplore in the multi-agent grid-world environments and show that it is compatible with both on-policy and off-policy MARL algorithms and be scalable to complex settings where agents' number or environment randomness increases.
Multi-task Learning for Bias-Free Joint CTR Prediction and Market Price Modeling in Online Advertising
Authors: Haizhi Yang, Tengyun Wang, Xiaoli Tang, Qianyu Li, Yueyue Shi, Siyu Jiang, Han Yu, Hengjie Song
Venue: CIKM
Year: 2021

The rapid rise of real-time bidding-based online advertising has brought significant economic benefits and attracted extensive research attention. From the perspective of an advertiser, it is crucial to perform accurate utility estimation and cost estimation for each individual auction in order to achieve cost-effective advertising. These problems are known as the click through rate (CTR) prediction task and the market price modeling task, respectively. However, existing approaches treat CTR prediction and market price modeling as two independent tasks to be optimized without regard to each other, thus resulting in suboptimal performance. Moreover, they do not make full use of unlabeled data from the losing bids during estimations, which makes them suffer from the sample selection bias issue. To address these limitations, we propose Multi-task Advertising Estimator (MTAE), an end-to-end joint optimization framework which performs both CTR prediction and market price modeling simultaneously. Through multi-task learning, both estimation tasks can take advantage of knowledge transfer to achieve improved feature representation and generalization abilities. In addition, we leverage the abundant bid price signals in the full-volume bid request data and introduce an auxiliary task of predicting the winning probability into the framework for unbiased learning. Through extensive experiments on two large-scale real-world public datasets, we demonstrate that our proposed approach has achieved significant improvements over the state-of-the-art models under various performance metrics.
Cascaded Deep Neural Ranking Models in LinkedIn People Search
Authors: Zimeng Yang, Song Yan, Abhimanyu Lad, Xiaowei Liu, Weiwei Guo
Venue: CIKM
Year: 2021

LinkedIn connects the world's professionals to make them more productive and successful. People Search plays an important role in fulfilling this goal by helping members find the most relevant and personalized results through a broad range of queries like names, job titles, skills, companies, locations, etc. It is one of the biggest search verticals at LinkedIn both in terms of engineering footprint and search traffic. In this paper, we present an overview of the People Search system, and discuss how we build and serve deep neural network (DNN) models, leveraging state-of-the-art deep natural language processing (NLP) techniques (e.g., convolutional neural networks (CNN) and Bidirectional Encoder Representations from Transformers (BERT)). We describe our journey of applying deep neural ranking models to a real-life product, including the modeling and system bottleneck challenges, crucial design choices, and lessons learned along the way. We hope a story of our endeavors and successes will provide meaningful insights to other similar systems.
HASTE: A Distributed System for Hybrid and Adaptive Processing on Streaming Spatial-Textual Data
Authors: Zhong Yang, Bolong Zheng, Chengdong Tong, Lianggui Weng, Chenliang Li, Guohui Li
Venue: CIKM
Year: 2021

Streaming spatial-textual data that contains geographic and textual information, e.g., geo-tagged tweets, has an unprecedented increase in amount. As one of the basic operations, the continuous spatial-textual queries that retrieve real-time results continuously on large-scale spatial-textual streams call for means of efficient distributed processing. However, existing proposals either are spatialaware only, or superficially exploit textual information for pruning. We propose a distributed system, called HASTE, for hybrid and adaptive processing on streaming spatial-textual data. The novelty lies on three aspects: (1) We propose a novel method to reduce the workload beforehand by dividing objects and queries into mutually exclusive types; (2) We develop a novel load partitioning strategy and a novel cost model that consider both spatial and textual properties; (3) We design a multi-level load adjustment strategy that adaptively copes with different degrees of load imbalance. We report on extensive experiments with real-world data that offer insight into the performance of the solution, and show that the solution is capable of outperforming the state-of-the-art proposals.
Cycle or Minkowski: Which is More Appropriate for Knowledge Graph Embedding?
Authors: Han Yang, Leilei Zhang, Bingning Wang, Ting Yao, Junfei Liu
Venue: CIKM
Year: 2021

Knowledge graph (KG) embedding aims to encode entities and relations into low-dimensional vector spaces, in turn, can support various machine learning models on KG related tasks with good performance. However, existing methods for knowledge graph embedding fail to consider the influence of the embedding space, which makes them still unsatisfactory in practical applications. In this study, we try to improve the expressiveness of the embedding space from the perspective of the metric. Specifically, we first point out the implications of Minkowski metric used in KG embedding and then make a quantitative analysis. To solve the limitations, we introduce a new metric, named Cycle metric, based on the oscillation property of the periodic function. Furthermore, we find that the function period has a significant influence on the expressiveness of the embedding space. Given a fully trained model, the smaller the period, the better the expressive ability. Finally, to validate the findings, we propose a new model, named CyclE by combining Cycle Metric and the popular KG embeddings models. Comprehensive experimental results show that Cycle is more appropriate than Minkowski for KG embedding.
An Interactive Neural Network Approach to Keyphrase Extraction in Talent Recruitment
Authors: Kaichun Yao, Chuan Qin, Hengshu Zhu, Chao Ma, Jingshuai Zhang, Yi Du, Hui Xiong
Venue: CIKM
Year: 2021

As a fundamental task of document content analysis, keyphrase extraction (KE) aims at predicting a set of lexical units that conveys the core information of the document. In this paper, we study the problem of KE in the talent recruitment. This problem is critical for the development of a variety of intelligent recruitment services, such as person-job fit, market trend analysis and course recommendation. However, unlike traditional textual data, the texts from the recruitment domain, such as resume and job postings, often have unique characteristics of abbreviation and succinctness, resulting in massive keyphrases consisting of inconsecutive words that are hard to be fully captured by existing KE methods. To this end, we propose an interactive neural network approach, INKE, for facilitating KE in the talent recruitment. To be specific, we first introduce a novel keyphrase indicator that captures the explicit hint information for each keyphrase. Then, we design a dynamically-initialized decoder which can generate keyphrases in an interactive manner. Moreover, we propose a hierarchical reinforcement learning algorithm to enhance the interaction between the hint information capture and keyphrase generation. Finally, extensive experiments on real-world data clearly validate the effectiveness and interpretability of INKE compared with state-of-the-art baselines.
AMPPERE: A Universal Abstract Machine for Privacy-Preserving Entity Resolution Evaluation
Authors: Yixiang Yao, Tanmay Ghai, Srivatsan Ravi, Pedro A. Szekely
Venue: CIKM
Year: 2021

Entity resolution is the task of identifying records in different datasets that refer to the same entity in the real world. In sensitive domains (e.g. financial accounts, hospital health records), entity resolution must meet privacy requirements to avoid revealing sensitive information such as personal identifiable information to untrusted parties. Existing solutions are either too algorithmically-specific or come with an implicit trade-off between accuracy of the computation, privacy, and run-time efficiency. We propose AMMPERE, an abstract computation model for performing universal privacy-preserving entity resolution. AMMPERE offers abstractions that encapsulate multiple algorithmic and platform-agnostic approaches using variants of Jaccard similarity to perform private data matching and entity resolution. Specifically, we show that two parties can perform entity resolution over their data, without leaking sensitive information. We rigorously compare and analyze the feasibility, performance overhead and privacy-preserving properties of these approaches on the Sharemind multi-party computation (MPC) platform as well as on PALISADE, a lattice-based homomorphic encryption library. The AMMPERE system demonstrates the efficacy of privacy-preserving entity resolution for real-world data while providing a precise characterization of the induced cost of preventing information leakage.
SCI: Subspace Learning Based Counterfactual Inference for Individual Treatment Effect Estimation
Authors: Liuyi Yao, Yaliang Li, Sheng Li, Mengdi Huai, Jing Gao, Aidong Zhang
Venue: CIKM
Year: 2021

Inferring causal effect from observational data has attracted much attention from various domains. Under the potential outcome framework, the estimation of counterfactuals is crucial for the investigation of causal effect at the individual level. Existing representation learning approaches focus on learning one balanced feature space, which ignores certain information predictive to the outcomes. To fully utilize the predictive information, we propose a Subspace learning based Counterfactual Inference (SCI) method to estimate causal effect at the individual level. Different from existing work, SCI learns both a common subspace, which preserves the information across all the treatment groups, and treatment-specific subspaces, which retain the information associated with each specific treatment. Learning from two kinds of subspaces helps SCI obtain better causal effect estimations than state-of-the-art methods, demonstrated by a series of experiments on synthetic and real-world datasets.
Task Allocation with Geographic Partition in Spatial Crowdsourcing
Authors: Guanyu Ye, Yan Zhao, Xuanhao Chen, Kai Zheng
Venue: CIKM
Year: 2021

Recent years have witnessed a revolution in Spatial Crowdsourcing (SC), in which people with mobile connectivity can perform spatio-temporal tasks that involve travel to specified locations. In this paper, we identify and study in depth a new multi-center-based task allocation problem in the context of SC, where multiple allocation centers exist. In particular, we aim to maximize the total number of the allocated tasks while minimizing the average allocated task number difference. To solve the problem, we propose a two-phase framework, called Task Allocation with Geographic Partition, consisting of a geographic partition phase and a task allocation phase. The first phase is to divide the whole study area based on the allocation centers by using both a basic Voronoi diagram-based algorithm and an adaptive weighted Voronoi diagram-based algorithm. In the allocation phase, we utilize a Reinforcement Learning method to achieve the task allocation, where a graph neural network with the attention mechanism is used to learn the embeddings of allocation centers, delivery points and workers. Extensive experiments give insight into the effectiveness and efficiency of the proposed solutions.
Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks
Authors: Chin-Chia Michael Yeh, Zhongfang Zhuang, Junpeng Wang, Yan Zheng, Javid Ebrahimi, Ryan Mercer, Liang Wang, Wei Zhang
Venue: CIKM
Year: 2021

Predicting metrics associated with entities' transnational behavior within payment processing networks is essential for system monitoring. Multivariate time series, aggregated from the past transaction history, can provide valuable insights for such prediction. The general multivariate time series prediction problem has been well studied and applied across several domains, including manufacturing, medical, and entomology. However, new domain-related challenges associated with the data such as concept drift and multi-modality have surfaced in addition to the real-time requirements of handling the payment transaction data at scale. In this work, we study the problem of multivariate time series prediction for estimating transaction metrics associated with entities in the payment transaction database. We propose a model with five unique components to estimate the transaction metrics from multi-modality data. Four of these components capture interaction, temporal, scale, and shape perspectives, and the fifth component fuses these perspectives together. We also propose a hybrid offline/online training scheme to address concept drift in the data and fulfill the real-time requirements. Combining the estimation model with a graphical user interface, the prototype transaction metric estimation system has demonstrated its potential benefit as a tool for improving a payment processing company's system monitoring capability.