Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining最新文献_第6页

Interactive Visualization of Urban Areas of Interest: A Parameter-Free and Efficient Footprint Method 城市兴趣区域的交互式可视化:一种无参数、高效的足迹方法

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290612

Mingzhao Li, Z. Bao, F. Choudhury, T. Sellis

引用次数: 0

Joint Optimization of Cascade Ranking Models 级联排序模型的联合优化

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290986

L. Gallagher, Ruey-Cheng Chen, Roi Blanco, J. Culpepper

{"title":"Joint Optimization of Cascade Ranking Models","authors":"L. Gallagher, Ruey-Cheng Chen, Roi Blanco, J. Culpepper","doi":"10.1145/3289600.3290986","DOIUrl":"https://doi.org/10.1145/3289600.3290986","url":null,"abstract":"Reducing excessive costs in feature acquisition and model evaluation has been a long-standing challenge in learning-to-rank systems. A cascaded ranking architecture turns ranking into a pipeline of multiple stages, and has been shown to be a powerful approach to balancing efficiency and effectiveness trade-offs in large-scale search systems. However, learning a cascade model is often complex, and usually performed stagewise independently across the entire ranking pipeline. In this work we show that learning a cascade ranking model in this manner is often suboptimal in terms of both effectiveness and efficiency. We present a new general framework for learning an end-to-end cascade of rankers using backpropagation. We show that stagewise objectives can be chained together and optimized jointly to achieve significantly better trade-offs globally. This novel approach is generalizable to not only differentiable models but also state-of-the-art tree-based algorithms such as LambdaMART and cost-efficient gradient boosted trees, and it opens up new opportunities for exploring additional efficiency-effectiveness trade-offs in large-scale search systems.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"70 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":"124609274","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: 30

A State Transition Model for Mobile Notifications via Survival Analysis 基于生存分析的移动通知状态转换模型

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290981

Yiping Yuan, Jing Zhang, S. Chatterjee, Shipeng Yu, Rómer Rosales

{"title":"A State Transition Model for Mobile Notifications via Survival Analysis","authors":"Yiping Yuan, Jing Zhang, S. Chatterjee, Shipeng Yu, Rómer Rosales","doi":"10.1145/3289600.3290981","DOIUrl":"https://doi.org/10.1145/3289600.3290981","url":null,"abstract":"Mobile notifications have become a major communication channel for social networking services to keep users informed and engaged. As more mobile applications push notifications to users, they constantly face decisions on what to send, when and how. A lack of research and methodology commonly leads to heuristic decision making. Many notifications arrive at an inappropriate moment or introduce too many interruptions, failing to provide value to users and spurring users' complaints. In this paper we explore unique features of interactions between mobile notifications and user engagement. We propose a state transition framework to quantitatively evaluate the effectiveness of notifications. Within this framework, we develop a survival model for badging notifications assuming a log-linear structure and a Weibull distribution. Our results show that this model achieves more flexibility for applications and superior prediction accuracy than a logistic regression model. In particular, we provide an online use case on notification delivery time optimization to show how we make better decisions, drive more user engagement, and provide more value to users.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"19 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":"125275001","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: 9

DAPA: The WSDM 2019 Workshop on Deep Matching in Practical Applications DAPA: WSDM 2019深度匹配实际应用研讨会

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291375

Yixing Fan, Qingyao Ai, Z. Ren, Liangjie Hong, Dawei Yin, J. Guo

引用次数: 2

Co-Embedding Attributed Networks 共嵌入属性网络

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291015

Zaiqiao Meng, Shangsong Liang, Hongyan Bao, Xiangliang Zhang

引用次数: 137

Deep Learning for Matching in Search and Recommendation 深度学习在搜索和推荐中的匹配

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291380

Jun Xu, Xiangnan He, Hang Li

{"title":"Deep Learning for Matching in Search and Recommendation","authors":"Jun Xu, Xiangnan He, Hang Li","doi":"10.1145/3289600.3291380","DOIUrl":"https://doi.org/10.1145/3289600.3291380","url":null,"abstract":"Matching is the key problem in search and recommendation, that is to measure the relevance of a document to a query or the interest of a user on an item. Previously, machine learning methods have been exploited to address the problem, which learn a matching function from labeled data, also referred to as \"learning to match\". In recent years, deep learning has been successfully applied to matching and significant progresses have been made. Deep semantic matching models for search and neural collaborative filtering models for recommendation are becoming the state-of-the-art technologies. The key to the success of the deep learning approach is its strong ability in learning of representations and generalization of matching patterns from raw data (e.g., queries, documents, users, and items, particularly in their raw forms). In this tutorial, we aim to give a comprehensive survey on recent progress in deep learning for matching in search and recommendation. Our tutorial is unique in that we try to give a unified view on search and recommendation. In this way, we expect researchers from the two fields can get deep understanding and accurate insight on the spaces, stimulate more ideas and discussions, and promote developments of technologies. The tutorial mainly consists of three parts. Firstly, we introduce the general problem of matching, which is fundamental in both search and recommendation. Secondly, we explain how traditional machine learning techniques are utilized to address the matching problems in search and recommendation. Lastly, we elaborate how deep learning can be effectively used to solve the matching problems in both tasks.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"80 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":"127721294","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: 7

Weakly Supervised Co-Training of Query Rewriting andSemantic Matching for e-Commerce 电子商务查询改写与语义匹配的弱监督协同训练

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291039

Rong Xiao, Jianhui Ji, Baoliang Cui, Haihong Tang, Wenwu Ou, Yanghua Xiao, Jiwei Tan, Xuan Ju

{"title":"Weakly Supervised Co-Training of Query Rewriting andSemantic Matching for e-Commerce","authors":"Rong Xiao, Jianhui Ji, Baoliang Cui, Haihong Tang, Wenwu Ou, Yanghua Xiao, Jiwei Tan, Xuan Ju","doi":"10.1145/3289600.3291039","DOIUrl":"https://doi.org/10.1145/3289600.3291039","url":null,"abstract":"Relevance is the core problem of a search engine, and one of the main challenges is the vocabulary gap between user queries and documents. This problem is more serious in e-commerce, because language in product titles is more professional. Query rewriting and semantic matching are two key techniques to bridge the semantic gap between them to improve relevance. Recently, deep neural networks have been successfully applied to the two tasks and enhanced the relevance performance. However, such approaches suffer from the sparseness of training data in e-commerce scenario. In this study, we investigate the instinctive connection between query rewriting and semantic matching tasks, and propose a co-training framework to address the data sparseness problem when training deep neural networks. We first build a huge unlabeled dataset from search logs, on which the two tasks can be considered as two different views of the relevance problem. Then we iteratively co-train them via labeled data generated from this unlabeled set to boost their performance simultaneously. We conduct a series of offline and online experiments on a real-world e-commerce search engine, and the results demonstrate that the proposed method improves relevance significantly.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"59 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":"128378370","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: 27

Privacy-preserving Data Mining in Industry 工业中保护隐私的数据挖掘

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291384

K. Kenthapadi, Ilya Mironov, Abhradeep Thakurta

引用次数: 24

Review-Driven Answer Generation for Product-Related Questions in E-Commerce 电子商务中产品相关问题的评论驱动答案生成

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290971

Shiqian Chen, Chenliang Li, Feng Ji, Wei Zhou, Haiqing Chen

{"title":"Review-Driven Answer Generation for Product-Related Questions in E-Commerce","authors":"Shiqian Chen, Chenliang Li, Feng Ji, Wei Zhou, Haiqing Chen","doi":"10.1145/3289600.3290971","DOIUrl":"https://doi.org/10.1145/3289600.3290971","url":null,"abstract":"The users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify the desired information. In this paper, we propose a novel review-driven framework for answer generation for product-related questions in E-commerce, named RAGE. We develope RAGE on the basis of the multi-layer convolutional architecture to facilitate speed-up of answer generation with the parallel computation. For each question, RAGE first extracts the relevant review snippets from the reviews of the corresponding product. Then, we devise a mechanism to identify the relevant information from the noise-prone review snippets and incorporate this information to guide the answer generation. The experiments on two real-world E-Commerce datasets show that the proposed RAGE significantly outperforms the existing alternatives in producing more accurate and informative answers in natural language. Moreover, RAGE takes much less time for both model training and answer generation than the existing RNN based generation models.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"59 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":"126278748","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: 37

Enabling Privacy-Preserving Sharing of Genomic Data for GWASs in Decentralized Networks 在去中心化网络中实现基因组数据的隐私保护共享

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290983

Yanjun Zhang, Xin Zhao, Xue Li, Mingyang Zhong, Caitlin I. Curtis, Chen Chen

引用次数: 14