Proceedings of the 2017 ACM on Conference on Information and Knowledge Management最新文献_第9页

Rapid Analysis of Network Connectivity 网络连通性快速分析

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133170

Scott Freitas, Hanghang Tong, Nan Cao, Yinglong Xia

引用次数: 3

BoostVHT: Boosting Distributed Streaming Decision Trees BoostVHT:增强分布式流决策树

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132974

Theodore Vasiloudis, F. Beligianni, G. D. F. Morales

引用次数: 8

Optimizing Email Volume For Sitewide Engagement 优化电子邮件量为全站参与

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132849

Rupesh Gupta, Guanfeng Liang, Rómer Rosales

{"title":"Optimizing Email Volume For Sitewide Engagement","authors":"Rupesh Gupta, Guanfeng Liang, Rómer Rosales","doi":"10.1145/3132847.3132849","DOIUrl":"https://doi.org/10.1145/3132847.3132849","url":null,"abstract":"In this paper we focus on the problem of optimizing email volume for maximizing sitewide engagement of an online social networking service. Email volume optimization approaches published in the past have proposed optimization of email volume for maximization of engagement metrics which are impacted exclusively by email; for example, the number of sessions that begin with clicks on links within emails. The impact of email on such downstream engagement metrics can be estimated easily because of the ease of attribution of such an engagement event to an email. However, this framework is limited in its view of the ecosystem of the networking service which comprises of several tools and utilities that contribute towards delivering value to members; with email being just one such utility. Thus, in this paper we depart from previous approaches by exploring and optimizing the contribution of email to this ecosystem. In particular, we present and contrast the differential impact of email on sitewide engagement metrics for various types of users. We propose a new email volume optimization approach which maximizes sitewide engagement metrics, such as the total number of active users. This is in sharp contrast to the previous approaches whose objective has been maximization of downstream engagement metrics. We present details of our prediction function for predicting the impact of emails on a user's activeness on the mobile or web application. We describe how certain approximations to this prediction function can be made for solving the volume optimization problem, and present results from online A/B tests.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"151 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79507874","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: 18

Estimating Event Focus Time Using Neural Word Embeddings 使用神经词嵌入估计事件焦点时间

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133131

Supratim Das, Arunav Mishra, K. Berberich, Vinay Setty

引用次数: 11

Collecting Non-Geotagged Local Tweets via Bandit Algorithms 通过Bandit算法收集非地理标记的本地tweet

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133046

Saki Ueda, Yuto Yamaguchi, H. Kitagawa

引用次数: 1

A Two-Stage Framework for Computing Entity Relatedness in Wikipedia 维基百科中计算实体关联的两阶段框架

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132890

Marco Ponza, P. Ferragina, Soumen Chakrabarti

引用次数: 21

A Matrix-Vector Recurrent Unit Model for Capturing Compositional Semantics in Phrase Embeddings 基于矩阵-向量循环单元模型的短语嵌入组合语义捕获

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132984

Rui Wang, Wei Liu, C. McDonald

{"title":"A Matrix-Vector Recurrent Unit Model for Capturing Compositional Semantics in Phrase Embeddings","authors":"Rui Wang, Wei Liu, C. McDonald","doi":"10.1145/3132847.3132984","DOIUrl":"https://doi.org/10.1145/3132847.3132984","url":null,"abstract":"The meaning of a multi-word phrase not only depends on the meaning of its constituent words, but also the rules of composing them to give the so-called compositional semantic. However, many deep learning models for learning compositional semantics target specific NLP tasks such as sentiment classification. Consequently, the word embeddings encode the lexical semantics, the weights of the networks are optimised for the classification task. Such models have no mechanisms to explicitly encode the compositional rules, and hence they are insufficient in capturing the semantics of phrases. We present a novel recurrent computational mechanism that specifically learns the compositionality by encoding the compositional rule of each word into a matrix. The network uses a recurrent architecture to capture the order of words for phrases with various lengths without requiring extra preprocessing such as part-of-speech tagging. The model is thoroughly evaluated on both supervised and unsupervised NLP tasks including phrase similarity, noun-modifier questions, sentiment distribution prediction, and domain specific term identification tasks. We demonstrate that our model consistently outperforms the LSTM and CNN deep learning models, simple algebraic compositions, and other popular baselines on different datasets.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74656988","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: 3

SemFacet: Making Hard Faceted Search Easier SemFacet:使硬面搜索更容易

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133192

E. Kharlamov, Luca Giacomelli, Evgeny Sherkhonov, B. C. Grau, Egor V. Kostylev, Ian Horrocks

引用次数: 20

Soft Seeded SSL Graphs for Unsupervised Semantic Similarity-based Retrieval 基于无监督语义相似度检索的软种子SSL图

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133162

Avikalp Srivastava, Madhav Datt

引用次数: 3

Personalized Image Aesthetics Assessment 个性化形象美学评价

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133052

Xian-Ping Deng, C. Cui, Huidi Fang, Xiushan Nie, Yilong Yin

引用次数: 14