Proceedings of the Ninth ACM International Conference on Web Search and Data Mining最新文献_第7页

You've got Mail, and Here is What you Could do With It!: Analyzing and Predicting Actions on Email Messages 你有邮件，这是你可以用它做什么!:分析和预测电子邮件消息的行为

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835811

Dotan Di Castro, Zohar S. Karnin, L. Lewin-Eytan, Y. Maarek

{"title":"You've got Mail, and Here is What you Could do With It!: Analyzing and Predicting Actions on Email Messages","authors":"Dotan Di Castro, Zohar S. Karnin, L. Lewin-Eytan, Y. Maarek","doi":"10.1145/2835776.2835811","DOIUrl":"https://doi.org/10.1145/2835776.2835811","url":null,"abstract":"With email traffic increasing, leading Web mail services have started to offer features that assist users in reading and processing their inboxes. One approach is to identify \"important\" messages, while a complementary one is to bundle messages, especially machine-generated ones, in pre-defined categories. We rather propose here to go back to the task at hand and consider what actions the users might conduct on received messages. We thoroughly studied, in a privacy-preserving manner, the actions of a large number of users in Yahoo mail, and found out that the most frequent actions are typically read, reply, delete and a sub-type of delete, delete-without-read. We devised a learning framework for predicting these four actions, for users with various levels of activity per action. Our framework leverages both vertical learning for personalization and horizontal learning for regularization purposes. In order to verify the quality of our predictions, we conducted a large-scale experiment involving users who had previously agreed to participate in such research studies. Our results show that, for recall values of 90%, we can predict important actions such as read or reply at precision levels up to 40% for active users, which we consider pretty encouraging for an assistance task. For less active users, we show that our regularization achieves an increase in AUC of close to 50%. To the best of our knowledge, our work is the first to provide a unified framework of this scale for predicting multiple actions on Web email, which hopefully provides a new ground for inventing new user experiences to help users process their inboxes.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74347878","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

Relational Learning with Social Status Analysis 关系学习与社会地位分析

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835782

Liang Wu, Xia Hu, Huan Liu

{"title":"Relational Learning with Social Status Analysis","authors":"Liang Wu, Xia Hu, Huan Liu","doi":"10.1145/2835776.2835782","DOIUrl":"https://doi.org/10.1145/2835776.2835782","url":null,"abstract":"Relational learning has been proposed to cope with the interdependency among linked instances in social network analysis, which often adopts network connectivity and social media content for prediction. A common assumption in existing relational learning methods is that data instances are equally important. The algorithms developed based on the assumption may be significantly affected by outlier data and thus less robust. In the meantime, it has been well established in social sciences that actors are naturally of different social status in a social network. Motivated by findings from social sciences, in this paper, we investigate whether social status analysis could facilitate relational learning. Particularly, we propose a novel framework RESA to model social status using the network structure. It extracts robust and intrinsic latent social dimensions for social actors, which are further exploited as features for supervised learning models. The proposed method is applicable for real-world relational learning problems where noise exists. Extensive experiments are conducted on datasets obtained from real-world social media platforms. Empirical results demonstrate the effectiveness of RESA and further experiments are conducted to help understand the effects of parameter settings to the proposed model and how local social status works.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77975780","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: 10

Session details: Communities and Social Interaction 会议细节:社区和社会互动

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/3253872

B. Aditya Prakash

引用次数: 0

Mobile App Tagging 移动应用标签

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835812

Ning Chen, S. Hoi, Shaohua Li, Xiaokui Xiao

{"title":"Mobile App Tagging","authors":"Ning Chen, S. Hoi, Shaohua Li, Xiaokui Xiao","doi":"10.1145/2835776.2835812","DOIUrl":"https://doi.org/10.1145/2835776.2835812","url":null,"abstract":"Mobile app tagging aims to assign a list of keywords indicating core functionalities, main contents, key features or concepts of a mobile app. Mobile app tags can be potentially useful for app ecosystem stakeholders or other parties to improve app search, browsing, categorization, and advertising, etc. However, most mainstream app markets, e.g., Google Play, Apple App Store, etc., currently do not explicitly support such tags for apps. To address this problem, we propose a novel auto mobile app tagging framework for annotating a given mobile app automatically, which is based on a search-based annotation paradigm powered by machine learning techniques. Specifically, given a novel query app without tags, our proposed framework (i) first explores online kernel learning techniques to retrieve a set of top-N similar apps that are semantically most similar to the query app from a large app repository; and (ii) then mines the text data of both the query app and the top-N similar apps to discover the most relevant tags for annotating the query app. To evaluate the efficacy of our proposed framework, we conduct an extensive set of experiments on a large real-world dataset crawled from Google Play. The encouraging results demonstrate that our technique is effective and promising.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89533682","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: 31

Towards Modelling Language Innovation Acceptance in Online Social Networks 论在线社交网络中语言创新的模式化接受

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835784

Daniel James Kershaw, Matthew Rowe, Patrick Stacey

{"title":"Towards Modelling Language Innovation Acceptance in Online Social Networks","authors":"Daniel James Kershaw, Matthew Rowe, Patrick Stacey","doi":"10.1145/2835776.2835784","DOIUrl":"https://doi.org/10.1145/2835776.2835784","url":null,"abstract":"Language change and innovation is constant in online and offline communication, and has led to new words entering people's lexicon and even entering modern day dictionaries, with recent additions of 'e-cig' and 'vape'. However the manual work required to identify these 'innovations' is both time consuming and subjective. In this work we demonstrate how such innovations in language can be identified across two different OSN's (Online Social Networks) through the operationalisation of known language acceptance models that incorporate relatively simple statistical tests. From grounding our work in language theory, we identified three statistical tests that can be applied - variation in; frequency, form and meaning. Each show different success rates across the two networks (Geo-bound Twitter sample and a sample of Reddit). These tests were also applied to different community levels within the two networks allowing for different innovations to be identified across different community structures over the two networks, for instance: identifying regional variation across Twitter, and variation across groupings of Subreddits, where identified example innovations included 'casualidad' and 'cym'.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84440479","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: 22

CCCF: Improving Collaborative Filtering via Scalable User-Item Co-Clustering CCCF:通过可扩展的用户-项目共聚类改进协同过滤

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835836

Yao Wu, Xudong Liu, Min Xie, M. Ester, Q. Yang

{"title":"CCCF: Improving Collaborative Filtering via Scalable User-Item Co-Clustering","authors":"Yao Wu, Xudong Liu, Min Xie, M. Ester, Q. Yang","doi":"10.1145/2835776.2835836","DOIUrl":"https://doi.org/10.1145/2835776.2835836","url":null,"abstract":"Collaborative Filtering (CF) is the most popular method for recommender systems. The principal idea of CF is that users might be interested in items that are favorited by similar users, and most of the existing CF methods measure users' preferences by their behaviours over all the items. However, users might have different interests over different topics, thus might share similar preferences with different groups of users over different sets of items. In this paper, we propose a novel and scalable method CCCF which improves the performance of CF methods via user-item co-clustering. CCCF first clusters users and items into several subgroups, where each subgroup includes a set of like-minded users and a set of items in which these users share their interests. Then, traditional CF methods can be easily applied to each subgroup, and the recommendation results from all the subgroups can be easily aggregated. Compared with previous works, CCCF has several advantages including scalability, flexibility, interpretability and extensibility. Experimental results on four real world data sets demonstrate that the proposed method significantly improves the performance of several state-of-the-art recommendation algorithms.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"88 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75888701","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: 56

Second Workshop on Search and Exploration of X-Rated Information (SEXI'16): WSDM Workshop Summary 第二次关于搜索和探索x级信息的研讨会(SEXI'16): WSDM研讨会总结

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2855118

Vanessa Murdock, C. Clarke, J. Kamps, Jussi Karlgren

引用次数: 2

Reducing Click and Skip Errors in Search Result Ranking 减少搜索结果排名中的点击和跳过错误

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835838

Jiepu Jiang, James Allan

引用次数: 5

Modeling Intransitivity in Matchup and Comparison Data 配对和比较数据中的非传递性建模

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835787

Shuo Chen, T. Joachims

引用次数: 74

Centrality-Aware Link Recommendations 中心性链接建议

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835818

Nikos Parotsidis, E. Pitoura, Panayiotis Tsaparas

{"title":"Centrality-Aware Link Recommendations","authors":"Nikos Parotsidis, E. Pitoura, Panayiotis Tsaparas","doi":"10.1145/2835776.2835818","DOIUrl":"https://doi.org/10.1145/2835776.2835818","url":null,"abstract":"Link recommendations are critical for both improving the utility and expediting the growth of social networks. Most previous approaches focus on suggesting links that are highly likely to be adopted. In this paper, we add a different perspective to the problem by aiming at recommending links that also improve specific properties of the network. In particular, our goal is to recommend to users links that if adopted would improve their centrality in the network. Specifically, we introduce the centrality-aware link recommendation problem as the problem of recommending to a user u, k links from a pool of recommended links so as to maximize the expected decrease of the sum of the shortest path distances of $u$ to all other nodes in the network. We show that the problem is NP-hard, but our optimization function is monotone and sub-modular which guarantees a constant approximation ratio for the greedy algorithm. We present a fast algorithm for computing the expected decrease caused by a set of recommendations which we use as a building block in our algorithms. We provide experimental results that evaluate the performance of our algorithms with respect to both the accuracy of the prediction and the improvement in the centrality of the nodes, and we study the tradeoff between the two.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81212908","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: 44