Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining最新文献_第9页

Modeling Time to Open of Emails with a Latent State for User Engagement Level 用潜在状态为用户粘性水平建模打开电子邮件的时间

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159683

Moumita Sinha, Vishwa Vinay, Harvineet Singh

{"title":"Modeling Time to Open of Emails with a Latent State for User Engagement Level","authors":"Moumita Sinha, Vishwa Vinay, Harvineet Singh","doi":"10.1145/3159652.3159683","DOIUrl":"https://doi.org/10.1145/3159652.3159683","url":null,"abstract":"Email messages have been an important mode of communication, not only for work, but also for social interactions and marketing. When messages have time sensitive information, it becomes relevant for the sender to know what is the expected time within which the email will be read by the recipient. In this paper we use a survival analysis framework to predict the time to open an email once it has been received. We use the Cox Proportional Hazards (CoxPH) model that offers a way to combine various features that might affect the event of opening an email. As an extension, we also apply a mixture model (MM) approach to CoxPH that distinguishes between recipients, based on a latent state of how prone to opening the messages each individual is. We compare our approach with standard classification and regression models. While the classification model provides predictions on the likelihood of an email being opened, the regression model provides prediction of the real-valued time to open. The use of survival analysis based methods allows us to jointly model both the open event as well as the time-to-open. We experimented on a large real-world dataset of marketing emails sent in a 3-month time duration. The mixture model achieves the best accuracy on our data where a high proportion of email messages go unopened.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"55 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123182178","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: 5

Deep Neural Architecture for Multi-Modal Retrieval based on Joint Embedding Space for Text and Images 基于联合嵌入空间的文本和图像多模态检索的深度神经结构

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159735

Saeid Balaneshin Kordan, Alexander Kotov

{"title":"Deep Neural Architecture for Multi-Modal Retrieval based on Joint Embedding Space for Text and Images","authors":"Saeid Balaneshin Kordan, Alexander Kotov","doi":"10.1145/3159652.3159735","DOIUrl":"https://doi.org/10.1145/3159652.3159735","url":null,"abstract":"Recent advances in deep learning and distributed representations of images and text have resulted in the emergence of several neural architectures for cross-modal retrieval tasks, such as searching collections of images in response to textual queries and assigning textual descriptions to images. However, the multi-modal retrieval scenario, when a query can be either a text or an image and the goal is to retrieve both a textual fragment and an image, which should be considered as an atomic unit, has been significantly less studied. In this paper, we propose a gated neural architecture to project image and keyword queries as well as multi-modal retrieval units into the same low-dimensional embedding space and perform semantic matching in this space. The proposed architecture is trained to minimize structured hinge loss and can be applied to both cross- and multi-modal retrieval. Experimental results for six different cross- and multi-modal retrieval tasks obtained on publicly available datasets indicate superior retrieval accuracy of the proposed architecture in comparison to the state-of-art baselines.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122774313","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

Joint Generative-Discriminative Aggregation Model for Multi-Option Crowd Labels 多选项人群标签的联合生成-判别聚合模型

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159672

Kamran Ghasedi Dizaji, Yanhua Yang, Heng Huang

{"title":"Joint Generative-Discriminative Aggregation Model for Multi-Option Crowd Labels","authors":"Kamran Ghasedi Dizaji, Yanhua Yang, Heng Huang","doi":"10.1145/3159652.3159672","DOIUrl":"https://doi.org/10.1145/3159652.3159672","url":null,"abstract":"Although some crowdsourcing aggregation models have been introduced to aggregate noisy crowd labels, these models mostly consider single-option (i.e. discrete) crowd labels as the input variables, and are not compatible with multi-option (i.e. non-deterministic) crowd data. In this paper, we propose a novel joint generative-discriminative aggregation model, which is able to efficiently deal with both single-option and multi-option crowd labels. Considering the confidence of workers for each option as the input data, we first introduce a new discriminative aggregation model, called Constrained Weighted Majority Voting (CWMVL1), which improves the performance of majority voting method. CWMVL1 considers flexible reliability parameters for crowd workers, employs L1-norm loss function to deal with noisy crowd data, and includes optimization constraints to have probabilistic outputs. We prove that our object is convex, and derive an efficient optimization algorithm. Moreover, we integrate the discriminative CWMVL1 model with a generative model, resulting in a powerful joint aggregation model. Combination of these sub-models is obtained in a probabilistic framework rather than a heuristic way. For our joint model, we derive an efficient optimization algorithm, which alternates between updating the parameters and estimating the potential true labels. Experimental results indicate that the proposed aggregation models achieve superior or competitive results in comparison with the state-of-the-art models on single-option and multi-option crowd datasets, while having faster convergence rates and more reliable predictions.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128599687","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: 2

Leveraging Implicit Contribution Amounts to Facilitate Microfinancing Requests 利用隐性捐款金额促进小额融资请求

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159679

Suhas Ranganath, Ghazaleh Beigi, Huan Liu

{"title":"Leveraging Implicit Contribution Amounts to Facilitate Microfinancing Requests","authors":"Suhas Ranganath, Ghazaleh Beigi, Huan Liu","doi":"10.1145/3159652.3159679","DOIUrl":"https://doi.org/10.1145/3159652.3159679","url":null,"abstract":"The emergence of online microfinancing platforms provides new opportunities for people to seek financial assistance from a large number of potential contributors. However, these platforms deal with a huge number of requests, making it hard for the requesters to get assistance for their financial needs. Designing algorithms to identify potential contributors for a given request will assist in satisfying financial needs of requesters and improve the effectiveness of microfinancing platforms. Existing work correlates requests with contributor interests and profiles to design feature based approaches for recommending projects to prospective contributors. However, contributing money to financial requests has a cost on contributors which can affect his inclination to contribute in the future . Literature in economic behavior has investigated the manner in which memory of past contribution amounts affects user inclination to contribute to a given request. To systematically investigate whether these characteristics of economic behavior would help to facilitate requests in online microfinancing platforms, we present a novel framework to identify contributors for a given request from their past financial information. Individual contribution amounts are not publicly available, so we draw from financial modeling literature to model the implicit contribution amounts made to past requests. We evaluate the framework on two microfinancing platforms to demonstrate its effectiveness in identifying contributors.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"513 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132592066","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: 0

Putting Data in the Driver's Seat: Optimizing Earnings for On-Demand Ride-Hailing 把数据放在驾驶座上:优化按需叫车服务的收益

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159721

Harshal A. Chaudhari, J. Byers, Evimaria Terzi

{"title":"Putting Data in the Driver's Seat: Optimizing Earnings for On-Demand Ride-Hailing","authors":"Harshal A. Chaudhari, J. Byers, Evimaria Terzi","doi":"10.1145/3159652.3159721","DOIUrl":"https://doi.org/10.1145/3159652.3159721","url":null,"abstract":"On-demand ride-hailing platforms like Uber and Lyft are helping reshape urban transportation, by enabling car owners to become drivers for hire with minimal overhead. Although there are many studies that consider ride-hailing platforms holistically, e.g., from the perspective of supply and demand equilibria, little emphasis has been placed on optimization for the individual, self-interested drivers that currently comprise these fleets. While some individuals drive opportunistically either as their schedule allows or on a fixed schedule, we show that strategic behavior regarding when and where to drive can substantially increase driver income. In this paper, we formalize the problem of devising a driver strategy to maximize expected earnings, describe a series of dynamic programming algorithms to solve these problems under different sets of modeled actions available to the drivers, and exemplify the models and methods on a large scale simulation of driving for Uber in NYC. In our experiments, we use a newly-collected dataset that combines the NYC taxi rides dataset along with Uber API data, to build time-varying traffic and payout matrices for a representative six-month time period in greater NYC. From this input, we can reason about prospective itineraries and payoffs. Moreover, the framework enables us to rigorously reason about and analyze the sensitivity of our results to perturbations in the input data. Among our main findings is that repositioning throughout the day is key to maximizing driver earnings, whereas »chasing surge' is typically misguided and sometimes a costly move.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133242133","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: 24

First Workshop on Knowledge Base Construction, Mining and Reasoning 第一届知识库构建、挖掘和推理研讨会

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3160596

Xiang Ren, Craig A. Knoblock, W. Wang, Yu Su

{"title":"First Workshop on Knowledge Base Construction, Mining and Reasoning","authors":"Xiang Ren, Craig A. Knoblock, W. Wang, Yu Su","doi":"10.1145/3159652.3160596","DOIUrl":"https://doi.org/10.1145/3159652.3160596","url":null,"abstract":"1. Motivation and Goals. e success of data mining and search technologies is largely aributed to the ecient and eective analysis of structured data. e construction of a well-structured, machine-actionable database from raw data sources is oen the premise of consequent applications. Meanwhile, the ability of mining and reasoning over such constructed databases is at the core of powering various downstream applications on web and mobile devices. Recently, we have witnessed a signicant amount of interests in building large-scale knowledge bases (KBs) from massive, unstructured data sources (e.g., Wikipedia-based methods such as DBpedia [9], YAGO [19], Wikidata [22], automated systems like Snowball [1], KnowItAll [5], NELL [4] and DeepDive [15], and opendomain approaches like Open IE [2] and Universal Schema [14]); as well as mining and reasoning over such knowledge bases to empower a wide variety of intelligent services, including question answering [6], recommender systems [3] and semantic search [8]. Automated construction, mining and reasoning of the knowledge bases have become possible as research advances in many related areas such as information extraction, natural language processing, data mining, search, machine learning, databases and data integration. However, there are still substantial scientic and engineering challenges in advancing and integrating such relevant methodologies. e goal of this proposed workshop is to gather together leading experts from industry and academia to share their visions about the eld, discuss latest research results, and exchange exciting ideas. With a focus on invited talks and position papers, the workshop aims to provide a vivid forum of discussion about knowledge base-related research. 2. Relevance to WSDM. Knowledge base construction, mining and reasoning is closely related to a wide variety of applications in WSDM, including web search, question answering, and recommender systems. Building a high-quality knowledge base from","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133991381","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

Discrete Deep Learning for Fast Content-Aware Recommendation 用于快速内容感知推荐的离散深度学习

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159688

Yan Zhang, Hongzhi Yin, Zi Huang, Xingzhong Du, Guowu Yang, Defu Lian

{"title":"Discrete Deep Learning for Fast Content-Aware Recommendation","authors":"Yan Zhang, Hongzhi Yin, Zi Huang, Xingzhong Du, Guowu Yang, Defu Lian","doi":"10.1145/3159652.3159688","DOIUrl":"https://doi.org/10.1145/3159652.3159688","url":null,"abstract":"Cold-start problem and recommendation efficiency have been regarded as two crucial challenges in the recommender system. In this paper, we propose a hashing based deep learning framework called Discrete Deep Learning (DDL), to map users and items to Hamming space, where a user»s preference for an item can be efficiently calculated by Hamming distance, and this computation scheme significantly improves the efficiency of online recommendation. Besides, DDL unifies the user-item interaction information and the item content information to overcome the issues of data sparsity and cold-start. To be more specific, to integrate content information into our DDL framework, a deep learning model, Deep Belief Network (DBN), is applied to extract effective item representation from the item content information. Besides, the framework imposes balance and irrelevant constraints on binary codes to derive compact but informative binary codes. Due to the discrete constraints in DDL, we propose an efficient alternating optimization method consisting of iteratively solving a series of mixed-integer programming subproblems. Extensive experiments have been conducted to evaluate the performance of our DDL framework on two different Amazon datasets, and the experimental results demonstrate the superiority of DDL over the state-of-the-art methods regarding online recommendation efficiency and cold-start recommendation accuracy.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115507350","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: 68

Influence Maximization in Online Social Networks 在线社交网络中的影响力最大化

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3162007

Çigdem Aslay, L. Lakshmanan, Weixu Lu, Xiaokui Xiao

{"title":"Influence Maximization in Online Social Networks","authors":"Çigdem Aslay, L. Lakshmanan, Weixu Lu, Xiaokui Xiao","doi":"10.1145/3159652.3162007","DOIUrl":"https://doi.org/10.1145/3159652.3162007","url":null,"abstract":"Starting with the earliest studies showing that the spread of new trends, information, and innovations is closely related to the social influence exerted on people by their social networks, the research on social influence theory took off, providing remarkable evidence on social influence induced viral phenomena. Fueled by the extreme popularity of online social networks and social media, computational social influence has emerged as a subfield of data mining whose goal is to analyze and optimize social influence using computational frameworks such as algorithm design and theoretical modeling. One of the fundamental problems in this field is the problem of influence maximization, primarily motivated by the application of viral marketing. The objective is to identify a small set of users in a social network who, when convinced to adopt a product, shall influence others in the network in a manner that leads to a large number of adoptions. In this tutorial, we extensively survey the research on social influence propagation and maximization, with a focus on the recent algorithmic and theoretical advances. To this end, we provide detailed reviews of the latest research effort devoted to (i) improving the efficiency and scalability of the influence maximization algorithms; (ii) context-aware modeling of the influence maximization problem to better capture real-world marketing scenarios; (iii) modeling and learning of real-world social influence; (iv) bridging the gap between social advertising and viral marketing.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127801979","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: 42

Latent Cross: Making Use of Context in Recurrent Recommender Systems 潜在交叉:在循环推荐系统中使用上下文

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159727

Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, Ed H. Chi

{"title":"Latent Cross: Making Use of Context in Recurrent Recommender Systems","authors":"Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, Ed H. Chi","doi":"10.1145/3159652.3159727","DOIUrl":"https://doi.org/10.1145/3159652.3159727","url":null,"abstract":"The success of recommender systems often depends on their ability to understand and make use of the context of the recommendation request. Significant research has focused on how time, location, interfaces, and a plethora of other contextual features affect recommendations. However, in using deep neural networks for recommender systems, researchers often ignore these contexts or incorporate them as ordinary features in the model. In this paper, we study how to effectively treat contextual data in neural recommender systems. We begin with an empirical analysis of the conventional approach to context as features in feed-forward recommenders and demonstrate that this approach is inefficient in capturing common feature crosses. We apply this insight to design a state-of-the-art RNN recommender system. We first describe our RNN-based recommender system in use at YouTube. Next, we offer \"Latent Cross,\" an easy-to-use technique to incorporate contextual data in the RNN by embedding the context feature first and then performing an element-wise product of the context embedding with model's hidden states. We demonstrate the improvement in performance by using this Latent Cross technique in multiple experimental settings.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123658323","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: 297

Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search 卷积神经网络在自组织搜索中的软匹配N-Grams

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159659

Zhuyun Dai, Chenyan Xiong, Jamie Callan, Zhiyuan Liu

引用次数: 300