{"title":"Gone, Gone, but Not Really, and Gone, But Not forgotten: A Typology of Website Recoverability","authors":"B. R. Ayala","doi":"10.1145/3543873.3587671","DOIUrl":"https://doi.org/10.1145/3543873.3587671","url":null,"abstract":"","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"1 1","pages":"1208-1213"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73535794","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}
{"title":"Those who are left behind: A chronicle of internet access in Cuba","authors":"B. R. Ayala","doi":"10.1145/3543873.3585573","DOIUrl":"https://doi.org/10.1145/3543873.3585573","url":null,"abstract":"","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"15 1","pages":"610-614"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90674649","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}
{"title":"Companion of The Web Conference 2022, Virtual Event / Lyon, France, April 25 - 29, 2022","authors":"","doi":"10.1145/3487553","DOIUrl":"https://doi.org/10.1145/3487553","url":null,"abstract":"","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88080433","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}
{"title":"Towards Automated Technologies in the Referencing Quality of Wikidata","authors":"Seyed Amir Hosseini Beghaeiraveri","doi":"10.1145/3487553.3524192","DOIUrl":"https://doi.org/10.1145/3487553.3524192","url":null,"abstract":"","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"33 2 1","pages":"324-328"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75984131","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}
{"title":"WWW '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021","authors":"","doi":"10.1145/3442381","DOIUrl":"https://doi.org/10.1145/3442381","url":null,"abstract":"","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86157077","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}
{"title":"Companion of The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021","authors":"","doi":"10.1145/3442442","DOIUrl":"https://doi.org/10.1145/3442442","url":null,"abstract":"","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86946960","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}
A body of law is an example of a dynamic corpus of text documents that are jointly maintained by a group of editors who compete and collaborate in complex constellations. Our goal is to develop predictive models for this process, thereby shedding light on the competitive dynamics of parliamentarians who make laws. For this purpose, we curated a dataset of 450000 legislative edits introduced by European parliamentarians over the last ten years. An edit modifies the status quo of a law, and could be in competition with another edit if it modifies the same part of that law. We propose a model for predicting the success of such edits, in the face of both the inertia of the status quo and the competition between overlapping edits. The parameters of this model can be interpreted in terms of the influence of parliamentarians and of the controversy of laws.
{"title":"War of Words: The Competitive Dynamics of Legislative Processes","authors":"Victor Kristof, M. Grossglauser, Patrick Thiran","doi":"10.1145/3366423.3380041","DOIUrl":"https://doi.org/10.1145/3366423.3380041","url":null,"abstract":"A body of law is an example of a dynamic corpus of text documents that are jointly maintained by a group of editors who compete and collaborate in complex constellations. Our goal is to develop predictive models for this process, thereby shedding light on the competitive dynamics of parliamentarians who make laws. For this purpose, we curated a dataset of 450000 legislative edits introduced by European parliamentarians over the last ten years. An edit modifies the status quo of a law, and could be in competition with another edit if it modifies the same part of that law. We propose a model for predicting the success of such edits, in the face of both the inertia of the status quo and the competition between overlapping edits. The parameters of this model can be interpreted in terms of the influence of parliamentarians and of the controversy of laws.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78692613","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}
The thriving of deep models and generative models provides approaches to model high dimensional distributions. Generative adversarial networks (GANs) can approximate data distributions and generate data samples from the learned data manifolds as well. In this paper, we propose an approach to estimate the implicit likelihoods of GAN models. A stable inverse function of the generator can be learned with the help of a variance network of the generator. The local variance of the sample distribution can be approximated by the normalized distance in the latent space. Simulation studies and likelihood testing on real-world data sets validate the proposed algorithm, which outperforms several baseline methods in these tasks. The proposed method has been further applied to anomaly detection. Experiments show that the method can achieve state-of-the-art anomaly detection performance on real-world data sets.
{"title":"Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection","authors":"Shaogang Ren, Dingcheng Li, Zhixin Zhou, P. Li","doi":"10.1145/3366423.3380293","DOIUrl":"https://doi.org/10.1145/3366423.3380293","url":null,"abstract":"The thriving of deep models and generative models provides approaches to model high dimensional distributions. Generative adversarial networks (GANs) can approximate data distributions and generate data samples from the learned data manifolds as well. In this paper, we propose an approach to estimate the implicit likelihoods of GAN models. A stable inverse function of the generator can be learned with the help of a variance network of the generator. The local variance of the sample distribution can be approximated by the normalized distance in the latent space. Simulation studies and likelihood testing on real-world data sets validate the proposed algorithm, which outperforms several baseline methods in these tasks. The proposed method has been further applied to anomaly detection. Experiments show that the method can achieve state-of-the-art anomaly detection performance on real-world data sets.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"211 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75136629","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}
With the advances of web-of-things, human mobility, e.g., GPS trajectories of vehicles, sharing bikes, and mobile devices, reflects people’s travel patterns and preferences, which are especially crucial for urban applications such as urban planning and business location selection. However, collecting a large set of human mobility data is not easy because of the privacy and commercial concerns, as well as the high cost to deploy sensors and a long time to collect the data, especially in newly developed cities. Realizing this, in this paper, based on the intuition that the human mobility is driven by the mobility intentions reflected by the origin and destination (or OD) features, as well as the preference to select the path between them, we investigate the problem to generate mobility data for a new target city, by transferring knowledge from mobility data and multi-source data of the source cities. Our framework contains three main stages: 1) mobility intention transfer, which learns a latent unified mobility intention distribution across the source cities, and transfers the model of the distribution to the target city; 2) OD generation, which generates the OD pairs in the target city based on the transferred mobility intention model, and 3) path generation, which generates the paths for each OD pair, based on a utility model learned from the real trajectory data in the source cities. Also, a demo of our trajectory generator is publicly available online for two city regions. Extensive experiment results over four regions in China validate the effectiveness of the proposed solution. Besides, an on-field case study is presented in a newly developed region, i.e., Xiongan, China. With the generated trajectories in the new city, many trajectory mining techniques can be applied.
{"title":"What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities","authors":"Tianfu He, Jie Bao, Ruiyuan Li, Sijie Ruan, Yanhua Li, Limei Song, Hui He, Yu Zheng","doi":"10.1145/3366423.3380210","DOIUrl":"https://doi.org/10.1145/3366423.3380210","url":null,"abstract":"With the advances of web-of-things, human mobility, e.g., GPS trajectories of vehicles, sharing bikes, and mobile devices, reflects people’s travel patterns and preferences, which are especially crucial for urban applications such as urban planning and business location selection. However, collecting a large set of human mobility data is not easy because of the privacy and commercial concerns, as well as the high cost to deploy sensors and a long time to collect the data, especially in newly developed cities. Realizing this, in this paper, based on the intuition that the human mobility is driven by the mobility intentions reflected by the origin and destination (or OD) features, as well as the preference to select the path between them, we investigate the problem to generate mobility data for a new target city, by transferring knowledge from mobility data and multi-source data of the source cities. Our framework contains three main stages: 1) mobility intention transfer, which learns a latent unified mobility intention distribution across the source cities, and transfers the model of the distribution to the target city; 2) OD generation, which generates the OD pairs in the target city based on the transferred mobility intention model, and 3) path generation, which generates the paths for each OD pair, based on a utility model learned from the real trajectory data in the source cities. Also, a demo of our trajectory generator is publicly available online for two city regions. Extensive experiment results over four regions in China validate the effectiveness of the proposed solution. Besides, an on-field case study is presented in a newly developed region, i.e., Xiongan, China. With the generated trajectories in the new city, many trajectory mining techniques can be applied.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"205 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77897015","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}
Visual aesthetics of products plays an important role in the decision process when purchasing appearance-first products, e.g., clothes. Indeed, user’s aesthetic preference, which serves as a personality trait and a basic requirement, is domain independent and could be used as a bridge between domains for knowledge transfer. However, existing work has rarely considered the aesthetic information in product images for cross-domain recommendation. To this end, in this paper, we propose a new deep Aesthetic Cross-Domain Networks (ACDN), in which parameters characterizing personal aesthetic preferences are shared across networks to transfer knowledge between domains. Specifically, we first leverage an aesthetic network to extract aesthetic features. Then, we integrate these features into a cross-domain network to transfer users’ domain independent aesthetic preferences. Moreover, network cross-connections are introduced to enable dual knowledge transfer across domains. Finally, the experimental results on real-world datasets show that our proposed model ACDN outperforms benchmark methods in terms of recommendation accuracy.
{"title":"Exploiting Aesthetic Preference in Deep Cross Networks for Cross-domain Recommendation","authors":"Jian Liu, Pengpeng Zhao, Fuzhen Zhuang, Yanchi Liu, V. Sheng, Jiajie Xu, Xiaofang Zhou, Hui Xiong","doi":"10.1145/3366423.3380036","DOIUrl":"https://doi.org/10.1145/3366423.3380036","url":null,"abstract":"Visual aesthetics of products plays an important role in the decision process when purchasing appearance-first products, e.g., clothes. Indeed, user’s aesthetic preference, which serves as a personality trait and a basic requirement, is domain independent and could be used as a bridge between domains for knowledge transfer. However, existing work has rarely considered the aesthetic information in product images for cross-domain recommendation. To this end, in this paper, we propose a new deep Aesthetic Cross-Domain Networks (ACDN), in which parameters characterizing personal aesthetic preferences are shared across networks to transfer knowledge between domains. Specifically, we first leverage an aesthetic network to extract aesthetic features. Then, we integrate these features into a cross-domain network to transfer users’ domain independent aesthetic preferences. Moreover, network cross-connections are introduced to enable dual knowledge transfer across domains. Finally, the experimental results on real-world datasets show that our proposed model ACDN outperforms benchmark methods in terms of recommendation accuracy.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78117823","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}
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