Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining最新文献_第5页

The 1st International Workshop on Context-Aware Recommendation Systems with Big Data Analytics (CARS-BDA) 第一届大数据分析情景感知推荐系统国际研讨会(CARS-BDA)

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291372

Xiangmin Zhou, Ji Zhang, Yanchun Zhang

{"title":"The 1st International Workshop on Context-Aware Recommendation Systems with Big Data Analytics (CARS-BDA)","authors":"Xiangmin Zhou, Ji Zhang, Yanchun Zhang","doi":"10.1145/3289600.3291372","DOIUrl":"https://doi.org/10.1145/3289600.3291372","url":null,"abstract":"With the explosive growth of online service platforms, increasing number of people and enterprises are doing everything online. In order for organizations, governments, and individuals to understand their users, and promote their products or services, it is necessary for them to analyse big data and recommend the media or online services in real time. Effective recommendation of items of interest to consumers has become critical for enterprises in domains such as retail, e-commerce, and online media. Driven by the business successes, academic research in this field has also been active for many years. Through many scientific breakthroughs have been achieved, there are still tremendous challenges in developing effective and scalable recommendation systems for real-world industrial applications. Existing solutions focus on recommending items based on pre-set contexts, such as time, location, weather etc. The big data sizes and complex contextual information add further challenges to the deployment of advanced recommender systems. This workshop aims to bring together researchers with wide-ranging backgrounds to identify important research questions, to exchange ideas from different research disciplines, and, more generally, to facilitate discussion and innovation in the area of context-aware recommender systems and big data analytics.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116399006","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

TopExNet: Entity-Centric Network Topic Exploration in News Streams TopExNet:新闻流中以实体为中心的网络主题探索

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290619

Andreas Spitz, Satya Almasian, Michael Gertz

引用次数: 7

Session details: Session 3: Recommendation and Temporal Trends 会议详情:第3部分:建议和时间趋势

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3310343

Flora D. Salim

引用次数: 0

Conducting Laboratory Experiments Properly with Statistical Tools: An Easy Hands-On Tutorial 正确使用统计工具进行实验室实验:一个简单的动手教程

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291378

T. Sakai

引用次数: 0

Privacy-Preserving WSDM 保护隐私WSDM

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291385

A. Korolova

引用次数: 1

Fast Dictionary-Based Compression for Inverted Indexes 基于字典的倒排索引快速压缩

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290962

Giulio Ermanno Pibiri, M. Petri, Alistair Moffat

引用次数: 18

Session details: Session 2: Knowledge Graphs and Analytics 会议详情:第二部分:知识图谱和分析

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3310342

Marc Najork

引用次数: 0

All Those Wasted Hours: On Task Abandonment in Crowdsourcing 所有被浪费的时间:关于众包中的任务放弃

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291035

Lei Han, Kevin Roitero, U. Gadiraju, Cristina Sarasua, Alessandro Checco, Eddy Maddalena, Gianluca Demartini

{"title":"All Those Wasted Hours: On Task Abandonment in Crowdsourcing","authors":"Lei Han, Kevin Roitero, U. Gadiraju, Cristina Sarasua, Alessandro Checco, Eddy Maddalena, Gianluca Demartini","doi":"10.1145/3289600.3291035","DOIUrl":"https://doi.org/10.1145/3289600.3291035","url":null,"abstract":"Crowdsourcing has become a standard methodology to collect manually annotated data such as relevance judgments at scale. On crowdsourcing platforms like Amazon MTurk or FigureEight, crowd workers select tasks to work on based on different dimensions such as task reward and requester reputation. Requesters then receive the judgments of workers who self-selected into the tasks and completed them successfully. Several crowd workers, however, preview tasks, begin working on them, reaching varying stages of task completion without finally submitting their work. Such behavior results in unrewarded effort which remains invisible to requesters. In this paper, we conduct the first investigation into the phenomenon of task abandonment, the act of workers previewing or beginning a task and deciding not to complete it. We follow a three-fold methodology which includes 1) investigating the prevalence and causes of task abandonment by means of a survey over different crowdsourcing platforms, 2) data-driven analyses of logs collected during a large-scale relevance judgment experiment, and 3) controlled experiments measuring the effect of different dimensions on abandonment. Our results show that task abandonment is a widely spread phenomenon. Apart from accounting for a considerable amount of wasted human effort, this bears important implications on the hourly wages of workers as they are not rewarded for tasks that they do not complete. We also show how task abandonment may have strong implications on the use of collected data (for example, on the evaluation of IR systems).","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131080616","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: 47

Spring-Electrical Models For Link Prediction 用于链路预测的弹簧电模型

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290961

Yana Kashinskaya, E. Samosvat, A. Artikov

引用次数: 3

Spiral of Silence in Recommender Systems 推荐系统中的沉默螺旋

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291003

Dugang Liu, Chen Lin, Zhilin Zhang, Yanghua Xiao, Hanghang Tong

{"title":"Spiral of Silence in Recommender Systems","authors":"Dugang Liu, Chen Lin, Zhilin Zhang, Yanghua Xiao, Hanghang Tong","doi":"10.1145/3289600.3291003","DOIUrl":"https://doi.org/10.1145/3289600.3291003","url":null,"abstract":"It has been established that, ratings are missing not at random in recommender systems. However, little research has been done to reveal how the ratings are missing. In this paper we present one possible explanation of the missing not at random phenomenon. We verify that, using a variety of different real-life datasets, there is a spiral process for a silent minority in recommender systems where (1) people whose opinions fall into the minority are less likely to give ratings than majority opinion holders; (2) as the majority opinion becomes more dominant, the rating possibility of a majority opinion holder is intensifying but the rating possibility of a minority opinion holder is shrinking; (3) only hardcore users remain to rate for minority opinions when the spiral achieves its steady state. Our empirical findings are beneficial for future recommendation models. To demonstrate the impact of our empirical findings, we present a probabilistic model that mimics the generation process of spiral of silence. We experimentally show that, the presented model offers more accurate recommendations, compared with state-of-the-art recommendation models.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126849338","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: 19