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

Modeling Check-in Preferences with Multidimensional Knowledge: A Minimax Entropy Approach 用多维知识建模签入偏好:一种极大极小熵方法

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835839

Jingjing Wang, Min Li, Jiawei Han, Xiaolong Wang

{"title":"Modeling Check-in Preferences with Multidimensional Knowledge: A Minimax Entropy Approach","authors":"Jingjing Wang, Min Li, Jiawei Han, Xiaolong Wang","doi":"10.1145/2835776.2835839","DOIUrl":"https://doi.org/10.1145/2835776.2835839","url":null,"abstract":"We propose a single unified minimax entropy approach for user preference modeling with multidimensional knowledge. Our approach provides a discriminative learning protocol which is able to simultaneously a) leverage explicit human knowledge, which are encoded as explicit features, and b) model the more ambiguous hidden intent, which are encoded as latent features. A latent feature can be carved by any parametric form, which allows it to accommodate arbitrary underlying assumptions. We present our approach in the scenario of check-in preference learning and demonstrate it is capable of modeling user preference in an optimized manner. Check-in preference is a fundamental component of Point-of-Interest (POI) prediction and recommendation. A user's check-in can be affected at multiple dimensions, such as the particular time, popularity of the place, his/her category and geographic preference, etc. With the geographic preferences modeled as latent features and the rest as explicit features, our approach provides an in-depth understanding of users' time-varying preferences over different POIs, as well as a reasonable representation of the hidden geographic clusters in a joint manner. Experimental results based on the task of POI prediction/recommendation with two real-world check-in datasets demonstrate that our approach can accurately model the check-in preferences and significantly outperforms the state-of-art models.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78930455","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

Dynamic Collective Entity Representations for Entity Ranking 用于实体排序的动态集体实体表示

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835819

David Graus, M. Tsagkias, W. Weerkamp, E. Meij, M. de Rijke

引用次数: 64

Probabilistic Group Recommendation Model for Crowdfunding Domains 众筹领域的概率群推荐模型

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835793

Vineeth Rakesh, Wang-Chien Lee, C. Reddy

{"title":"Probabilistic Group Recommendation Model for Crowdfunding Domains","authors":"Vineeth Rakesh, Wang-Chien Lee, C. Reddy","doi":"10.1145/2835776.2835793","DOIUrl":"https://doi.org/10.1145/2835776.2835793","url":null,"abstract":"Crowdfunding has gained a widespread popularity by fueling the creative minds of entrepreneurs. Not only has it democratized the funding of startups, it has also bridged the gap between the venture capitalists and the entrepreneurs by providing a plethora of opportunities for people seeking to invest in new business ventures. Nonetheless, despite the huge success of the crowdfunding platforms, not every project reaches its funding goal. One of the main reasons for a project's failure is the difficulty in establishing a linkage between it's founders and those investors who are interested in funding such projects. A potential solution to this problem is to develop recommendation systems that suggest suitable projects to crowdfunding investors by capturing their interests. In this paper, we explore Kickstarter, a popular reward-based crowdfunding platform. Being a highly heterogeneous platform, Kickstarter is fuelled by a dynamic community of people who constantly interact with each other before investing in projects. Therefore, the decision to invest in a project depends not only on the preference of individuals, but also on the influence of groups that a person belongs and the on-going status of the projects. In this paper, we propose a probabilistic recommendation model, called CrowdRec, that recommends Kickstarter projects to a group of investors by incorporating the on-going status of projects, the personal preference of individual members, and the collective preference of the group . Using a comprehensive dataset of over 40K crowdfunding groups and 5K projects, we show that our model is effective in recommending projects to groups of Kickstarter users.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83220327","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: 69

Mining Complaints to Improve a Product: a Study about Problem Phrase Extraction from User Reviews 挖掘投诉以改进产品:用户评论问题短语提取研究

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2855080

E. Tutubalina

引用次数: 1

Multileave Gradient Descent for Fast Online Learning to Rank 多叶梯度下降快速在线学习排名

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835804

Anne Schuth, Harrie Oosterhuis, Shimon Whiteson, M. de Rijke

{"title":"Multileave Gradient Descent for Fast Online Learning to Rank","authors":"Anne Schuth, Harrie Oosterhuis, Shimon Whiteson, M. de Rijke","doi":"10.1145/2835776.2835804","DOIUrl":"https://doi.org/10.1145/2835776.2835804","url":null,"abstract":"Modern search systems are based on dozens or even hundreds of ranking features. The dueling bandit gradient descent (DBGD) algorithm has been shown to effectively learn combinations of these features solely from user interactions. DBGD explores the search space by comparing a possibly improved ranker to the current production ranker. To this end, it uses interleaved comparison methods, which can infer with high sensitivity a preference between two rankings based only on interaction data. A limiting factor is that it can compare only to a single exploratory ranker. We propose an online learning to rank algorithm called multileave gradient descent (MGD) that extends DBGD to learn from so-called multileaved comparison methods that can compare a set of rankings instead of merely a pair. We show experimentally that MGD allows for better selection of candidates than DBGD without the need for more comparisons involving users. An important implication of our results is that orders of magnitude less user interaction data is required to find good rankers when multileaved comparisons are used within online learning to rank. Hence, fewer users need to be exposed to possibly inferior rankers and our method allows search engines to adapt more quickly to changes in user preferences.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77993446","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: 88

Your Cart tells You: Inferring Demographic Attributes from Purchase Data 您的购物车告诉您:从购买数据推断人口统计属性

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835783

Pengfei Wang, J. Guo, Yanyan Lan, Jun Xu, Xueqi Cheng

{"title":"Your Cart tells You: Inferring Demographic Attributes from Purchase Data","authors":"Pengfei Wang, J. Guo, Yanyan Lan, Jun Xu, Xueqi Cheng","doi":"10.1145/2835776.2835783","DOIUrl":"https://doi.org/10.1145/2835776.2835783","url":null,"abstract":"Demographic attributes play an important role in retail market to characterize different types of users. Such signals however are often only available for a small fraction of users in practice due to the difficulty in manual collection process by retailers. In this paper, we aim to harness the power of big data to automatically infer users' demographic attributes based on their purchase data. Typically, demographic prediction can be formalized as a multi-task multi-class prediction problem, i.e., multiple demographic attributes (e.g., gender, age and income) are to be inferred for each user where each attribute may belong to one of N possible classes (N-2). Most previous work on this problem explores different types of features and usually predicts different attributes independently. However, modeling the tasks separately may lose the ability to leverage the correlations among different attributes. Meanwhile, manually defined features require professional knowledge and often suffer from under specification. To address these problems, we propose a novel Structured Neural Embedding (SNE) model to automatically learn the representations from users' purchase data for predicting multiple demographic attributes simultaneously. Experiments are conducted on a real-world retail dataset where five attributes (gender, marital status, income, age, and education level) are to be predicted. The empirical results show that our SNE model can improve the performance significantly compared with state-of-the-art baselines.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"149 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78144766","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: 62

Session details: Practice & Experience Track 会议详情:实践与经验专场

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/3253874

M. Lalmas

引用次数: 0

Nonlinear Laplacian for Digraphs and its Applications to Network Analysis 有向图的非线性拉普拉斯及其在网络分析中的应用

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835785

Yuichi Yoshida

引用次数: 19

Hierarchical Label Propagation and Discovery for Machine Generated Email 机器生成电子邮件的分层标签传播和发现

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835780

James Bradley Wendt, Michael Bendersky, Lluis Garcia Pueyo, V. Josifovski, Balint Miklos, Ivo Krka, Amitabh Saikia, Jie Yang, Marc-Allen Cartright, Sujith Ravi

{"title":"Hierarchical Label Propagation and Discovery for Machine Generated Email","authors":"James Bradley Wendt, Michael Bendersky, Lluis Garcia Pueyo, V. Josifovski, Balint Miklos, Ivo Krka, Amitabh Saikia, Jie Yang, Marc-Allen Cartright, Sujith Ravi","doi":"10.1145/2835776.2835780","DOIUrl":"https://doi.org/10.1145/2835776.2835780","url":null,"abstract":"Machine-generated documents such as email or dynamic web pages are single instantiations of a pre-defined structural template. As such, they can be viewed as a hierarchy of template and document specific content. This hierarchical template representation has several important advantages for document clustering and classification. First, templates capture common topics among the documents, while filtering out the potentially noisy variabilities such as personal information. Second, template representations scale far better than document representations since a single template captures numerous documents. Finally, since templates group together structurally similar documents, they can propagate properties between all the documents that match the template. In this paper, we use these advantages for document classification by formulating an efficient and effective hierarchical label propagation and discovery algorithm. The labels are propagated first over a template graph (constructed based on either term-based or topic-based similarities), and then to the matching documents. We evaluate the performance of the proposed algorithm using a large donated email corpus and show that the resulting template graph is significantly more compact than the corresponding document graph and the hierarchical label propagation is both efficient and effective in increasing the coverage of the baseline document classification algorithm. We demonstrate that the template label propagation achieves more than 91% precision and 93% recall, while increasing the label coverage by more than 11%.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90681636","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

Hierarchical Semi-supervised Classification with Incomplete Class Hierarchies 不完全类层次的层次半监督分类

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835810

Bhavana Dalvi, A. Mishra, William W. Cohen

引用次数: 20