OpenRaiser/Intern-Atlas huggingface dataset:
Knowledge Graph Reasoning with Relational Digraph
Authors: Yongqi Zhang, Quanming Yao
Venue: WWW
Year: 2022

Reasoning on the knowledge graph (KG) aims to infer new facts from existing ones. Methods based on the relational path have shown strong, interpretable, and transferable reasoning ability. However, paths are naturally limited in capturing local evidence in graphs. In this paper, we introduce a novel relational structure, i.e., relational directed graph (r-digraph), which is composed of overlapped relational paths, to capture the KG’s local evidence. Since the r-digraphs are more complex than paths, how to efficiently construct and effectively learn from them are challenging. Directly encoding the r-digraphs cannot scale well and capturing query-dependent information is hard in r-digraphs. We propose a variant of graph neural network, i.e., RED-GNN, to address the above challenges. Specifically, RED-GNN makes use of dynamic programming to recursively encodes multiple r-digraphs with shared edges, and utilizes query-dependent attention mechanism to select the strongly correlated edges. We demonstrate that RED-GNN is not only efficient but also can achieve significant performance gains in both inductive and transductive reasoning tasks over existing methods. Besides, the learned attention weights in RED-GNN can exhibit interpretable evidence for KG reasoning. 1
Neuro-Symbolic Interpretable Collaborative Filtering for Attribute-based Recommendation
Authors: Wei Zhang, Junbing Yan, Zhuo Wang, Jianyong Wang
Venue: WWW
Year: 2022

Recommender System (RS) is ubiquitous on today’s Internet to provide multifaceted personalized information services. While an enormous success has been made in pushing forward high-accuracy recommendations, the other side of the coin — the recommendation explainability — needs to be better handled for pursuing persuasiveness, especially for the era of deep learning based recommendation. A few research efforts investigate interpretable recommendation from the feature and result levels. Compared with them, model-level explanation, which unfolds the reasoning process of recommendation through transparent models, still remains underexplored and deserves more attention. In this paper, we propose a model-based explainable recommendation approach, i.e., NS-ICF, which stands for Neuro-Symbolic Interpretable Collaborative Filtering. Thanks to the recent advance on neuro-symbolic computation for automatic rule learning, NS-ICF learns interpretable recommendation rules (consisting of user and item attributes) based on neural networks with two innovations: (1) a three-tower architecture tailored for the user and item sides in the RS domain; (2) fusing the powerful personalized representations of users and items to achieve adaptive rule weights and without sacrificing interpretability. Comprehensive experiments on public datasets demonstrate NS-ICF is comparable to state-of-the-art deep recommendation models and is transparent for its unique neuro-symbolic architecture.
Graph-adaptive Rectified Linear Unit for Graph Neural Networks
Authors: Yifei Zhang, Hao Zhu, Ziqiao Meng, Piotr Koniusz, Irwin King
Venue: WWW
Year: 2022

Graph Neural Networks (GNNs) have achieved remarkable success by extending traditional convolution to learning on non-Euclidean data. The key to the GNNs is adopting the neural message-passing paradigm with two stages: aggregation and update. The current design of GNNs considers the topology information in the aggregation stage. However, in the updating stage, all nodes share the same updating function. The identical updating function treats each node embedding as i.i.d. random variables and thus ignores the implicit relationships between neighborhoods, which limits the capacity of the GNNs. The updating function is usually implemented with a linear transformation followed by a non-linear activation function. To make the updating function topology-aware, we inject the topological information into the non-linear activation function and propose Graph-adaptive Rectified Linear Unit (GReLU), which is a new parametric activation function incorporating the neighborhood information in a novel and efficient way. The parameters of GReLU are obtained from a hyperfunction based on both node features and the corresponding adjacent matrix. To reduce the risk of overfitting and the computational cost, we decompose the hyperfunction as two independent components for nodes and features respectively. We conduct comprehensive experiments to show that our plug-and-play GReLU method is efficient and effective given different GNN backbones and various downstream tasks.
Beyond Bot Detection: Combating Fraudulent Online Survey Takers✱
Authors: Ziyi Zhang, Shuofei Zhu, Jaron Mink, Aiping Xiong, Linhai Song, Gang Wang
Venue: WWW
Year: 2022

Different techniques have been recommended to detect fraudulent responses in online surveys, but little research has been taken to systematically test the extent to which they actually work in practice. In this paper, we conduct an empirical evaluation of 22 anti-fraud tests in two complementary online surveys. The first survey recruits Rust programmers on public online forums and social media networks. We find that fraudulent respondents involve both bot and human characteristics. Among different anti-fraud tests, those designed based on domain knowledge are the most effective. By combining individual tests, we can achieve a detection performance as good as commercial techniques while making the results more explainable. To explore these tests under a broader context, we ran a different survey on Amazon Mechanical Turk (MTurk). The results show that for a generic survey without requiring users to have any domain knowledge, it is more difficult to distinguish fraudulent responses. However, a subset of tests still remain effective.
Expressing Metaphorically, Writing Creatively: Metaphor Identification for Creativity Assessment in Writing
Authors: Dongyu Zhang, Minghao Zhang, Ciyuan Peng, Feng Xia
Venue: WWW
Year: 2022

Metaphor, which can implicitly express profound meanings and emotions, is a unique writing technique frequently used in human language. In writing, meaningful metaphorical expressions can enhance the literariness and creativity of texts. Therefore, the usage of metaphor is a significant impact factor when assessing the creativity and literariness of writing. However, little to no automatic writing assessment system considers metaphorical expressions when giving the score of creativity. For improving the accuracy of automatic writing assessment, this paper proposes a novel creativity assessment model that imports a token-level metaphor identification method to extract metaphors as the indicators for creativity scoring. The experimental results show that our model can accurately assess the creativity of different texts with precise metaphor identification. To the best of our knowledge, we are the first to apply automatic metaphor identification to assess writing creativity. Moreover, identifying features (e.g., metaphors) that influence writing creativity using computational approaches can offer fair and reliable assessment methods for educational settings.
FastClip: An Efficient Video Understanding System with Heterogeneous Computing and Coarse-to-fine Processing
Authors: Liming Zhao, Siyang Sun, Yanhao Zhang, Yun Zheng, Pan Pan
Venue: WWW
Year: 2022

Recently, video medias are exponentially growing in many areas such as E-commerce shopping and gaming. Understanding the video contents is critical for real-world applications. However, processing long videos is usually time-consuming and expensive. In this paper, we present an efficient video understanding system, which aims to speed up the video processing with a coarse-to-fine two-stage pipeline and heterogeneous computing framework. First, we use a coarse but fast multi-modal filtering module to recognize and remove useless video segments from a long video, which could be deployed on an edge device and reduce computations for the next processing. Second, several semantic models are applied for finely parsing the remained sequences. To accelerate the model inference, we propose a novel heterogeneous computing framework, which trains a model with lightweight and heavyweight backbones to support a distributed deployment on a powerful device (e.g., cloud or GPU) and another different device (e.g., edge or CPU). In this way, the model could be both efficient and effective. The proposed system has been widely used in Alibaba, including “Taobao Live Analysis” and “Commodity Short-Video Generation”, which could achieve a 10 × speedup for the system.
Knowledge-aware Conversational Preference Elicitation with Bandit Feedback
Authors: Canzhe Zhao, Tong Yu, Zhihui Xie, Shuai Li
Venue: WWW
Year: 2022

Conversational recommender systems (CRSs) have been proposed recently to mitigate the cold-start problem suffered by the traditional recommender systems. By introducing conversational key-terms, existing conversational recommenders can effectively reduce the need for extensive exploration and elicit the user preferences faster and more accurately. However, existing conversational recommenders leveraging key-terms heavily rely on the availability and quality of the key-terms, and their performances might degrade significantly when the key-terms are incomplete or not well labeled, which usually happens when there are new items being consistently incorporated into the systems and involving lots of human efforts to acquire well-labeled key-terms is costly. Besides, existing CRS methods leverage the feedback to different conversational key-terms separately, without considering the underlying relations between the key-terms. In this case, the learning of the conversational recommenders is sample inefficient, especially when there is a large number of candidate conversational key-terms. In this paper, we propose a knowledge-aware conversational preference elicitation framework and a bandit-based algorithm GraphConUCB. To achieve efficient preference elicitation given items with incompletely labeled key-terms, our algorithm leverage the underlying relations between the key-terms, guided by the knowledge graph. Being knowledge-aware, our algorithm propagates the user preferences via a pseudo graph feedback module, which also accelerates the exploration in the large action space of key-terms and improves the conversational sample efficiency. To select the most informative conversational key-terms in the graphs to conduct conversations, we further devise a graph-based optimal design module which leverages the graph structure. We provide the theoretical analysis of the regret upper bound for GraphConUCB. With extensive experiments, we show that our algorithm can effectively handle the items with incompletely labeled key-terms, and improves over the state-of-the-art baselines significantly.
Winning Tracker: A New Model for Real-time Winning Prediction in MOBA Games
Authors: Chuang Zhao, Hongke Zhao, Yong Ge, Runze Wu, Xudong Shen
Venue: WWW
Year: 2022

With an increasing popularity, Multiplayer Online Battle Arena (MOBA) games where two opposing teams compete against each other, have played a major role in E-sports tournaments. Among game analysis, real-time winning prediction is an important but challenging problem, which is mainly due to the complicated coupling of the overall Confrontation1, the excessive noise of the player’s Movement, and unclear optimization goals. Existing research is difficult to solve this problem in a dynamic, comprehensive and systematic way. In this study, we design a unified framework, namely Winning Tracker (WT), for solving this problem. Specifically, offense and defense extractors are developed to extract the Confrontation of both sides. A well-designed trajectory representation algorithm is applied to extracting individual’s Movement information. Moreover, we design a hierarchical attention mechanism to capture team-level strategies and facilitate the interpretability of the framework. To optimize accurately, we adopt a multi-task learning method to design short-term and long-term goals, which are used to represent immediate state and make end-state prediction respectively. Intensive experiments on a real-world data set demonstrate that our proposed method WT outperforms state-of-the-art algorithms. Furthermore, our work has been practically deployed in real MOBA games, and provided case studies reflecting its outstanding commercial value.
Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads
Authors: Kaifu Zheng, Lu Wang, Yu Li, Xusong Chen, Hu Liu, Jing Lu, Xiwei Zhao, Changping Peng, Zhangang Lin, Jingping Shao
Venue: WWW
Year: 2022

Click-through rate (CTR) prediction plays a crucial role in sponsored search advertising (search ads). User click behavior usually showcases strong comparison patterns among relevant/competing items within the user awareness. Explicit user awareness could be characterized by user behavior sequence modeling, which however suffers from issues such as cold start, behavior noise and hidden channels. Instead, in this paper, we study the problem of modeling implicit user awareness about relevant/competing items. We notice that candidate items of the CTR prediction model could play as surrogates for relevant/competing items within the user awareness. Motivated by this finding, we propose a novel framework, named CIM (Candidate Item Modeling), to characterize users’ awareness on candidate items. CIM introduces an additional module to encode candidate items into a context vector and therefore is plug-and-play for existing neural network-based CTR prediction models. Offline experiments on a ten-billion-scale production dataset collected from the real traffic of a search advertising system, together with the corresponding online A/B testing, demonstrate CIM’s superior performance. Notably, CIM has been deployed in production at JD.com, serving the main traffic of hundreds of millions of users, which shows great application value. Our code and dataset are available at https://github.com/kaifuzheng/cim.
Is Least-Squares Inaccurate in Fitting Power-Law Distributions? The Criticism is Complete Nonsense
Authors: Xiaoshi Zhong, Muyin Wang, Hongkun Zhang
Venue: WWW
Year: 2022

Ordinary least-squares estimation is proved to be the best linear unbiased estimator according to the Gauss-Markov theorem. In the last two decades, however, some researchers criticized that least-squares was substantially inaccurate in fitting power-law distributions; such criticism has caused a strong bias in research community. In this paper, we conduct extensive experiments to rebut that such criticism is complete nonsense. Specifically, we sample different sizes of discrete and continuous data from power-law models, showing that even though the long-tailed noises are sampled from power-law models, they cannot be treated as power-law data. We define the correct way to bin continuous power-law data into data points and propose an average strategy for least-squares to fit power-law distributions. Experiments on both simulated and real-world data show that our proposed method fits power-law data perfectly. We uncover a fundamental flaw in the popular method proposed by Clauset et al. [12]: it tends to discard the majority of power-law data and fit the long-tailed noises. Experiments also show that the reverse cumulative distribution function is a bad idea to plot power-law data in practice because it usually hides the true probability distribution of data. We hope that our research can clean up the bias about least-squares fitting power-law distributions. Source code can be found at https://github.com/xszhong/LSavg.
"This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders
Authors: Xinyi Zhou, Kai Shu, Vir V. Phoha, Huan Liu, Reza Zafarani
Venue: WWW
Year: 2022

Individuals can be misled by fake news and spread it unintentionally without knowing it is false. This phenomenon has been frequently observed but has not been investigated. Our aim in this work is to assess the intent of fake news spreaders. To distinguish between intentional versus unintentional spreading, we study the psychological explanations of unintentional spreading. With this foundation, we then propose an influence graph, using which we assess the intent of fake news spreaders. Our extensive experiments show that the assessed intent can help significantly differentiate between intentional and unintentional fake news spreaders. Furthermore, the estimated intent can significantly improve the current techniques that detect fake news. To our best knowledge, this is the first work to model individuals’ intent in fake news spreading.
Emotion Bubbles: Emotional Composition of Online Discourse Before and After the COVID-19 Outbreak
Authors: Assem Zhunis, Gabriel Lima, Hyeonho Song, Jiyoung Han, Meeyoung Cha
Venue: WWW
Year: 2022

The COVID-19 pandemic has been the single most important global agenda in the past two years. In addition to its health and economic impacts, it has affected people’s psychological states, including a rise in depression and domestic violence. We traced how the overall emotional states of individual Twitter users changed before and after the pandemic. Our data, including more than 9 million tweets posted by 9,493 users, suggest that the threat posed by the virus did not upset the emotional equilibrium of social media. In early 2020, COVID-related tweets skyrocketed in number and were filled with negative emotions; however, this emotional outburst was short-lived. We found that users who had expressed positive emotions in the pre-COVID period remained positive after the initial outbreak, while the opposite was true for those who regularly expressed negative emotions. Individuals achieved such emotional consistency by selectively focusing on emotion-reinforcing topics. The implications are discussed in light of an emotionally motivated confirmation bias, which we conceptualize as emotion bubbles that demonstrate the public’s resilience to a global health risk.
Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning
Authors: Shweta Yadav, Stefan Cobeli, Cornelia Caragea
Venue: WWW
Year: 2023

In recent years, seeking health information on the web has become a preferred way for healthcare consumers to support their information needs. Generally, healthcare consumers use long and detailed questions with several peripheral details to express their healthcare concerns, contributing to natural language understanding challenges. One way to address this challenge is by summarizing the questions. However, most of the existing abstractive summarization systems generate impeccably fluent yet factually incorrect summaries. In this paper, we present a semantically-enhanced contrastive learning-based framework for generating abstractive question summaries that are faithful and factually correct. We devised multiple strategies based on question semantics to generate the erroneous (negative) summaries, such that the model has the understanding of plausible and incorrect perturbations of the original summary. Our extensive experimental results on two benchmark consumer health question summarization datasets confirm the effectiveness of our proposed method by achieving state-of-the-art performance and generating factually correct and fluent summaries, as measured by human evaluation.
Implementing an Environmental Management System Using Provenance-By-Design
Authors: Luc Moreau, Nicola Hogan, Nick O&apos, Donnell
Venue: WWW
Year: 2023

Organisations have to comply with environmental regulations to protect the environment and meet internationally agreed climate change targets. To assist organisations, processes and standards are being defined to manage these compliance obligations. They typically rely on a notion of Environmental Management System (EMS), defined as a reflective framework allowing organisations to set and manage their goals, and demonstrate they follow due processes in order to comply with prevailing regulations. The importance of these obligations can be highlighted by the fact that failing to comply may lead to significant liabilities for organisations. An EMS framework, typically structured as a set of documents and spreadsheets, contains a record of continuously evolving regulations, teams, stakeholders, actions and updates. However, the maintainance of an EMS is often human driven, and therefore is error prone despite the meticulousness of environmental officers, and further requires external human auditing to check their validity. To avoid green washing, but also to contain the burden and cost of compliance, it is desirable for these claims to be checked by trusted automated means. Provenance is ideally suited to track the changes occurring in an EMS, allowing queries to determine precisely which compliance objective is prevailing at any point in time, whether it is being met, and who is responsible for it. Thus, this paper has a dual aim: first, it investigates the benefits of provenance for EMS, second, it presents the application of an emerging approach “Provenance-By-Design”, which automatically converts a specification of an EMS data model and its provenance to a data backend, a service for processing and querying of EMS provenance data, a client-side library to interact with such a service, and a simple user interface allowing developers to navigate the provenance. The application of a Provenance-By-Design approach to EMS applications results in novel opportunities for a provenance-based EMS; we present our preliminary reflection on their potential.
Multi-aspect Diffusion Network Inference
Authors: Hao Huang, Keqi Han, Beicheng Xu, Ting Gan
Venue: WWW
Year: 2023

To learn influence relationships between nodes in a diffusion network, most existing approaches resort to precise timestamps of historical node infections. The target network is customarily assumed as an one-aspect diffusion network, with homogeneous influence relationships. Nonetheless, tracing node infection timestamps is often infeasible due to high cost, and the type of influence relationships may be heterogeneous because of the diversity of propagation media. In this work, we study how to infer a multi-aspect diffusion network with heterogeneous influence relationships, using only node infection statuses that are more readily accessible in practice. Equipped with a probabilistic generative model, we iteratively conduct a posteriori, quantitative analysis on historical diffusion results of the network, and infer the structure and strengths of homogeneous influence relationships in each aspect. Extensive experiments on both synthetic and real-world networks are conducted, and the results verify the effectiveness and efficiency of our approach.