Proceedings of the Web Conference 2021最新文献_第8页

Keyword-aware Abstractive Summarization by Extracting Set-level Intermediate Summaries 基于集级中间摘要提取的关键字感知抽象摘要

Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442381.3449906

Yizhu Liu, Qi Jia, Kenny Q. Zhu

{"title":"Keyword-aware Abstractive Summarization by Extracting Set-level Intermediate Summaries","authors":"Yizhu Liu, Qi Jia, Kenny Q. Zhu","doi":"10.1145/3442381.3449906","DOIUrl":"https://doi.org/10.1145/3442381.3449906","url":null,"abstract":"Abstractive summarization is useful in providing a summary or a digest of news or other web texts and enhancing users reading experience, especially when they are reading on small displays such as mobile phones. However, existing encoder-decoder summarization models have difficulty learning the latent alignment between source documents and summaries because of their vast disparity in length. In this paper, we propose a extractor-abstractor framework in which the keyword-based extractor selects a few sets of salient sentences from the input document and then the abstractor paraphrases these sets of sentences in parallel, which are more aligned to the summary, to generate the final summary. The new extractor and abstractor are pretrained from a set of “pseudo summaries” extracted by specially designed heuristics, and then further trained together in a reinforcement learning framework. The results show that the proposed model generates high-quality summaries with faster training speed and less training memory footprint, and outperforms the state-of-the-art models on CNN/Daily Mail, Webis-TLDR-17, Webis-Snippet-20, WikiHow and DUC-2002 datasets.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117093276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Learning Dynamic User Behavior Based on Error-driven Event Representation 基于错误驱动事件表示的动态用户行为学习

Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442381.3450012

Honglian Wang, Peiyan Li, Wujun Tao, Bailin Feng, Junming Shao

{"title":"Learning Dynamic User Behavior Based on Error-driven Event Representation","authors":"Honglian Wang, Peiyan Li, Wujun Tao, Bailin Feng, Junming Shao","doi":"10.1145/3442381.3450012","DOIUrl":"https://doi.org/10.1145/3442381.3450012","url":null,"abstract":"Understanding the evolution of large graphs over time is of significant importance in user behavior understanding and prediction. Modeling user behavior with temporal networks has gained increasing attention in recent years since it allows capturing users’ dynamic preferences and predicting their next actions. Recently, some approaches have been proposed to model user behavior. However, these methods suffer from two problems: they work on static data, which ignores the dynamic evolution, or they model the whole behavior sequences directly by recurrent neural networks and thus suffer from noisy information. To tackle these problems, we propose a dynamic user behavior learning algorithm called LDBR. It views user behaviors as a set of dynamic events and uses recent event embedding to predict future user behavior and infer the current semantic labels. Specifically, we propose a new strategy to automatically learn a good event embedding in behavior sequence by introducing a smooth sampling strategy and minimizing the temporal link prediction error. It is hard to obtain real-world datasets with evolving labels. Thus in this paper, we provide a new dynamic network dataset with evolving labels called Arxiv and make it publicly available. Based on the Arxiv dataset, we conduct a case study to verify the quality of event embedding. Extensive experiments on temporal link prediction tasks further demonstrate the effectiveness of the LDBR model.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114360264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Robust Network Alignment via Attack Signal Scaling and Adversarial Perturbation Elimination 基于攻击信号缩放和对抗扰动消除的鲁棒网络对齐

Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442381.3449823

Yang Zhou, Zeru Zhang, Sixing Wu, Victor S. Sheng, Xiaoying Han, Zijie Zhang, R. Jin

{"title":"Robust Network Alignment via Attack Signal Scaling and Adversarial Perturbation Elimination","authors":"Yang Zhou, Zeru Zhang, Sixing Wu, Victor S. Sheng, Xiaoying Han, Zijie Zhang, R. Jin","doi":"10.1145/3442381.3449823","DOIUrl":"https://doi.org/10.1145/3442381.3449823","url":null,"abstract":"Recent studies have shown that graph learning models are highly vulnerable to adversarial attacks, and network alignment methods are no exception. How to enhance the robustness of network alignment against adversarial attacks remains an open research problem. In this paper, we propose a robust network alignment solution, RNA, for offering preemptive protection of existing network alignment algorithms, enhanced with the guidance of effective adversarial attacks. First, we analyze how popular iterative gradient-based adversarial attack techniques suffer from gradient vanishing issues and show a fake sense of attack effectiveness. Based on dynamical isometry theory, an attack signal scaling (ASS) method with established upper bound of feasible signal scaling is introduced to alleviate the gradient vanishing issues for effective adversarial attacks while maintaining the decision boundary of network alignment. Second, we develop an adversarial perturbation elimination (APE) model to neutralize adversarial nodes in vulnerable space to adversarial-free nodes in safe area, by integrating Dirac delta approximation (DDA) techniques and the LSTM models. Our proposed APE method is able to provide proactive protection to existing network alignment algorithms against adversarial attacks. The theoretical analysis demonstrates the existence of an optimal distribution for the APE model to reach a lower bound. Last but not least, extensive evaluation on real datasets presents that RNA is able to offer the preemptive protection to trained network alignment methods against three popular adversarial attack models.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127595577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Incrementality Testing in Programmatic Advertising: Enhanced Precision with Double-Blind Designs 程序化广告的增量测试:用双盲设计提高精确度

Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442381.3450106

Joel Barajas, Narayan L. Bhamidipati

{"title":"Incrementality Testing in Programmatic Advertising: Enhanced Precision with Double-Blind Designs","authors":"Joel Barajas, Narayan L. Bhamidipati","doi":"10.1145/3442381.3450106","DOIUrl":"https://doi.org/10.1145/3442381.3450106","url":null,"abstract":"Measuring the incremental value of advertising (incrementality) is critical for financial planning and budget allocation by advertisers. Running randomized controlled experiments is the gold standard in marketing incrementality measurement. Current literature and industry practices to run incrementality experiments focus on running placebo, intention-to-treat (ITT), or ghost bidding based experiments. A fundamental challenge with these is that the serving engine as treatment administrator is not blind to the user treatment assignment. Similarly, ITT and ghost bidding solutions provide greatly decreased precision since many experiment users never see ads. We present a novel randomized design solution for incrementality testing based on ghost bidding with improved measurement precision. Our design provides faster and cheaper results including double-blind, to the users and to the serving engine, post-auction experiment execution without ad targeting bias. We also identify ghost impressions in open ad exchanges by matching the bidding values or ads sent to external auctions with held-out bid values. This design leads to larger precision than ITT or current ghost bidding solutions. Our proposed design has been fully deployed in a real production system within a commercial programmatic ad network combined with a Demand Side Platform (DSP) that places ad bids in third-party ad exchanges. We have found reductions of up to 85% of the advertiser budget to reach statistical significance with typical ghost bids conversion and winner rates. Moreover, the highest statistical power at 50% control size design of this current practice is reached at 8% of our proposed design. By deploying this design, for an advertiser in the insurance industry, to measure the incrementality of display and native programmatic advertising, we have found conclusive evidence that the last-touch attribution framework (current industry standard) undervalues these channels by 87% when compared to the incremental conversions derived from the experiment.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128067233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Unifying Offline Causal Inference and Online Bandit Learning for Data Driven Decision 统一离线因果推理和在线数据驱动决策的强盗学习

Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442381.3449982

Ye Li, Hong Xie, Yishi Lin, John C.S. Lui

引用次数: 8

Touch Screen Exploration of Visual Artwork for Blind People 盲人视觉艺术的触摸屏探索

Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442381.3449871

D. Ahmetovic, Nahyun Kwon, U. Oh, C. Bernareggi, S. Mascetti

{"title":"Touch Screen Exploration of Visual Artwork for Blind People","authors":"D. Ahmetovic, Nahyun Kwon, U. Oh, C. Bernareggi, S. Mascetti","doi":"10.1145/3442381.3449871","DOIUrl":"https://doi.org/10.1145/3442381.3449871","url":null,"abstract":"This paper investigates how touchscreen exploration and verbal feedback can be used to support blind people to access visual artwork. We present two artwork exploration modalities. The first one, attribute-based exploration, extends prior work on touchscreen image accessibility, and provides fine-grained segmentation of artwork visual elements; when the user touches an element, the associated attributes are read. The second one, hierarchical exploration, is designed with domain experts and provides multi-level segmentation of the artwork; the user initially accesses a general description of the entire artwork and then explores a coarse segmentation of the visual elements with the corresponding high-level descriptions; once selected, coarse segments are subdivided into fine-grained ones, which the user can access for more detailed descriptions. The two exploration modalities, implemented as a mobile web app, were evaluated through a user study with 10 blind participants. Both modalities were appreciated by the participants. Attribute-based exploration is perceived to be easier to access. Instead, the hierarchical exploration was considered more understandable, useful, interesting and captivating, and the participants remembered more details about the artwork with this modality. Participants commented that the two modalities work well together and therefore both should be made available.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126351323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

ReACt: A Resource-centric Access Control System for Web-app Interactions on Android ReACt:一个以资源为中心的访问控制系统，用于Android上的web应用交互

Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442381.3449960

Xin Zhang, Yifan Zhang

{"title":"ReACt: A Resource-centric Access Control System for Web-app Interactions on Android","authors":"Xin Zhang, Yifan Zhang","doi":"10.1145/3442381.3449960","DOIUrl":"https://doi.org/10.1145/3442381.3449960","url":null,"abstract":"We identify and survey five mechanisms through which web content interacts with mobile apps. While useful, these web-app interaction mechanisms cause various notable security vulnerabilities on mobile apps or web content. The root cause is lack of proper access control mechanisms for web-app interactions on mobile OSes. Existing solutions usually adopt either an origin-centric design or a code-centric design, and suffer from one or several of the following limitations: coarse protection granularity, poor flexibility in terms of access control policy establishment, and incompatibility with existing apps/OSes due to the need of modifying the apps and/or the underlying OS. More importantly, none of the existing works can organically deal with all the five web-app interaction mechanisms. In this paper, we propose ReACt, a novel Resource-centric Access Control design that can coherently work with all the web-app interaction mechanisms while addressing the above-mentioned limitations. We have implemented a prototype system on Android, and performed extensive evaluation on it. The evaluation results show that our system works well with existing commercial off-the-shelf Android apps and different versions of Android OS, and it can achieve the design goals with small overhead.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126532283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Multilingual COVID-QA: Learning towards Global Information Sharing via Web Question Answering in Multiple Languages 多语言COVID-QA:通过多语言网络问答学习实现全球信息共享

Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442381.3449991

Rui Yan, Weiheng Liao, Jianwei Cui, Hailei Zhang, Yichuan Hu, Dongyan Zhao

{"title":"Multilingual COVID-QA: Learning towards Global Information Sharing via Web Question Answering in Multiple Languages","authors":"Rui Yan, Weiheng Liao, Jianwei Cui, Hailei Zhang, Yichuan Hu, Dongyan Zhao","doi":"10.1145/3442381.3449991","DOIUrl":"https://doi.org/10.1145/3442381.3449991","url":null,"abstract":"Since late December 2019, it has been reported an outbreak of atypical pneumonia, now known as COVID-19 caused by the novel coronavirus. Cases have spread to more than 200 countries and regions internationally. World Health Organization (WHO) officially declares the coronavirus outbreak a pandemic and the public health emergency has caused world-wide impact to daily lives: people are advised to keep social distance, in-person events have been moved online, and some function facilitates have been locked-down. Alternatively, the Web becomes an active venue for people to share information. With respect to the on-going topic, people continuously post questions online and seek for answers. Yet, sharing global information conveyed in different languages is challenging because the language barrier is intrinsically unfriendly to monolingual speakers. In this paper, we propose a multilingual COVID-QA model to answer people’s questions in their own languages while the model is able to absorb knowledge from other languages. Another challenge is that in most cases, the information to share does not have parallel data in multiple languages. To this end, we propose a novel framework which incorporates (unsupervised) translation alignment to learn as pseudo-parallel data. Then we train multilingual question-answering mapping and generation. We demonstrate the effectiveness of our proposed approach compared against a series of competitive baselines. In this way, we make it easier to share global information across the language barriers, and hopefully we contribute to the battle against COVID-19.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125176348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Where To Next? A Dynamic Model of User Preferences 下一个目标是什么?用户偏好的动态模型

Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442381.3450028

Francesco Sanna Passino, Lucas Maystre, Dmitrii Moor, Ashton Anderson, M. Lalmas

{"title":"Where To Next? A Dynamic Model of User Preferences","authors":"Francesco Sanna Passino, Lucas Maystre, Dmitrii Moor, Ashton Anderson, M. Lalmas","doi":"10.1145/3442381.3450028","DOIUrl":"https://doi.org/10.1145/3442381.3450028","url":null,"abstract":"We consider the problem of predicting users’ preferences on online platforms. We build on recent findings suggesting that users’ preferences change over time, and that helping users expand their horizons is important in ensuring that they stay engaged. Most existing models of user preferences attempt to capture simultaneous preferences: “Users who like A tend to like B as well”. In this paper, we argue that these models fail to anticipate changing preferences. To overcome this issue, we seek to understand the structure that underlies the evolution of user preferences. To this end, we propose the Preference Transition Model (PTM), a dynamic model for user preferences towards classes of items. The model enables the estimation of transition probabilities between classes of items over time, which can be used to estimate how users’ tastes are expected to evolve based on their past history. We test our model’s predictive performance on a number of different prediction tasks on data from three different domains: music streaming, restaurant recommendations and movie recommendations, and find that it outperforms competing approaches. We then focus on a music application, and inspect the structure learned by our model. We find that the PTM uncovers remarkable regularities in users’ preference trajectories over time. We believe that these findings could inform a new generation of dynamic, diversity-enhancing recommender systems.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124135166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Debiasing Career Recommendations with Neural Fair Collaborative Filtering 基于神经公平协同过滤的职业推荐去偏

Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442381.3449904

Rashidul Islam, Kamrun Keya, Ziqian Zeng, Shimei Pan, James R. Foulds

引用次数: 46