Proceedings of The Web Conference 2020最新文献

Gone, Gone, but Not Really, and Gone, But Not forgotten: A Typology of Website Recoverability 走了，走了，但不是真的，走了，但没有被遗忘:网站可恢复性的类型学

Proceedings of The Web Conference 2020 Pub Date : 2023-01-01 DOI: 10.1145/3543873.3587671

B. R. Ayala

引用次数: 0

Those who are left behind: A chronicle of internet access in Cuba 那些被遗忘的人:古巴互联网接入的编年史

Proceedings of The Web Conference 2020 Pub Date : 2023-01-01 DOI: 10.1145/3543873.3585573

B. R. Ayala

引用次数: 0

Companion of The Web Conference 2022, Virtual Event / Lyon, France, April 25 - 29, 2022 网络会议2022的伴侣，虚拟事件/里昂，法国，2022年4月25日至29日

Proceedings of The Web Conference 2020 Pub Date : 2022-01-01 DOI: 10.1145/3487553

引用次数: 0

Towards Automated Technologies in the Referencing Quality of Wikidata 面向自动化技术的维基数据引用质量研究

Proceedings of The Web Conference 2020 Pub Date : 2022-01-01 DOI: 10.1145/3487553.3524192

Seyed Amir Hosseini Beghaeiraveri

引用次数: 0

WWW '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021 WWW '21:网络会议2021，虚拟事件/卢布尔雅那，斯洛文尼亚，2021年4月19日至23日

Proceedings of The Web Conference 2020 Pub Date : 2021-01-01 DOI: 10.1145/3442381

引用次数: 2

Companion of The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021 网络会议2021的伴侣，虚拟事件/卢布尔雅那，斯洛文尼亚，2021年4月19日至23日

Proceedings of The Web Conference 2020 Pub Date : 2021-01-01 DOI: 10.1145/3442442

引用次数: 2

War of Words: The Competitive Dynamics of Legislative Processes 口水战:立法过程的竞争动态

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380041

Victor Kristof, M. Grossglauser, Patrick Thiran

引用次数: 4

Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection 估计gan的隐似然及其在异常检测中的应用

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380293

Shaogang Ren, Dingcheng Li, Zhixin Zhou, P. Li

引用次数: 10

What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities 什么是新城市中的人的流动性:跨城市的流动性知识传递

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380210

Tianfu He, Jie Bao, Ruiyuan Li, Sijie Ruan, Yanhua Li, Limei Song, Hui He, Yu Zheng

{"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}

引用次数: 26

Exploiting Aesthetic Preference in Deep Cross Networks for Cross-domain Recommendation 利用深度交叉网络中的审美偏好进行跨领域推荐

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380036

Jian Liu, Pengpeng Zhao, Fuzhen Zhuang, Yanchi Liu, V. Sheng, Jiajie Xu, Xiaofang Zhou, Hui Xiong

{"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}

引用次数: 28