Proceedings of the sixth ACM international conference on Web search and data mining最新文献

Learning to rank for spatiotemporal search 学习对时空搜索进行排序

Proceedings of the sixth ACM international conference on Web search and data mining Pub Date : 2013-02-04 DOI: 10.1145/2433396.2433485

B. Shaw, Jon Shea, Siddhartha Sinha, A. Hogue

{"title":"Learning to rank for spatiotemporal search","authors":"B. Shaw, Jon Shea, Siddhartha Sinha, A. Hogue","doi":"10.1145/2433396.2433485","DOIUrl":"https://doi.org/10.1145/2433396.2433485","url":null,"abstract":"In this article we consider the problem of mapping a noisy estimate of a user's current location to a semantically meaningful point of interest, such as a home, restaurant, or store. Despite the poor accuracy of GPS on current mobile devices and the relatively high density of places in urban areas, it is possible to predict a user's location with considerable precision by explicitly modeling both places and users and by combining a variety of signals about a user's current context. Places are often simply modeled as a single latitude and longitude when in fact they are complex entities existing in both space and time and shaped by the millions of people that interact with them. Similarly, models of users reveal complex but predictable patterns of mobility that can be exploited for this task. We propose a novel spatial search algorithm that infers a user's location by combining aggregate signals mined from billions of foursquare check-ins with real-time contextual information. We evaluate a variety of techniques and demonstrate that machine learning algorithms for ranking and spatiotemporal models of places and users offer significant improvement over common methods for location search based on distance and popularity.","PeriodicalId":324799,"journal":{"name":"Proceedings of the sixth ACM international conference on Web search and data mining","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114672605","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: 97

Data-driven political science 数据驱动的政治学

Proceedings of the sixth ACM international conference on Web search and data mining Pub Date : 2013-02-04 DOI: 10.1145/2433396.2433498

Ingmar Weber, Ana-Maria Popescu, M. Pennacchiotti

{"title":"Data-driven political science","authors":"Ingmar Weber, Ana-Maria Popescu, M. Pennacchiotti","doi":"10.1145/2433396.2433498","DOIUrl":"https://doi.org/10.1145/2433396.2433498","url":null,"abstract":"The tutorial will summarize the state-of-the art in the growing area of computational political science. Like many others, this research domain is being revolutionized by the availability of open, big data and the increasing reach and importance of social media. The surging interest on the part of the academic community is matched by intense efforts on the part of political campaigns to use online data in order to learn how to best disseminate information and reach the right potential donors or voters. In this context, a tutorial can summarize existing methods in a fascinating, high-interest area and allow participants with diverse backgrounds to get inspiration from the methods and problems studied. The tutorial will feature seminal research concerning (i) political polarization, (ii) election prediction and polling, and (iii) political campaigning and influence propagation. The goal is not only to familiarize attendees with ideas from related conferences such as WWW, ICWSM or CIKM, but also to present ideas and quantitative methods closer to political science such as Poole's and Rosenthal's NOMINATE score for a politician's political orientation.","PeriodicalId":324799,"journal":{"name":"Proceedings of the sixth ACM international conference on Web search and data mining","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125121647","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: 6

Connecting comments and tags: improved modeling of social tagging systems 连接评论和标签:改进的社会标签系统建模

Proceedings of the sixth ACM international conference on Web search and data mining Pub Date : 2013-02-04 DOI: 10.1145/2433396.2433466

Dawei Yin, Shengbo Guo, Boris Chidlovskii, Brian D. Davison, C. Archambeau, Guillaume Bouchard

{"title":"Connecting comments and tags: improved modeling of social tagging systems","authors":"Dawei Yin, Shengbo Guo, Boris Chidlovskii, Brian D. Davison, C. Archambeau, Guillaume Bouchard","doi":"10.1145/2433396.2433466","DOIUrl":"https://doi.org/10.1145/2433396.2433466","url":null,"abstract":"Collaborative tagging systems are now deployed extensively to help users share and organize resources. Tag prediction and recommendation can simplify and streamline the user experience, and by modeling user preferences, predictive accuracy can be significantly improved. However, previous methods typically model user behavior based only on a log of prior tags, neglecting other behaviors and information in social tagging systems, e.g., commenting on items and connecting with other users. On the other hand, little is known about the connection and correlations among these behaviors and contexts in social tagging systems. In this paper, we investigate improved modeling for predictive social tagging systems. Our explanatory analyses demonstrate three significant challenges: coupled high order interaction, data sparsity and cold start on items. We tackle these problems by using a generalized latent factor model and fully Bayesian treatment. To evaluate performance, we test on two real-world data sets from Flickr and Bibsonomy. Our experiments on these data sets show that to achieve best predictive performance, it is necessary to employ a fully Bayesian treatment in modeling high order relations in social tagging system. Our methods noticeably outperform state-of-the-art approaches.","PeriodicalId":324799,"journal":{"name":"Proceedings of the sixth ACM international conference on Web search and data mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129442210","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: 32

Advanced graph mining for community evaluation in social networks and the web 社会网络和web中用于社区评估的高级图挖掘

Proceedings of the sixth ACM international conference on Web search and data mining Pub Date : 2013-02-04 DOI: 10.1145/2433396.2433495

C. Giatsidis, Fragkiskos D. Malliaros, M. Vazirgiannis

{"title":"Advanced graph mining for community evaluation in social networks and the web","authors":"C. Giatsidis, Fragkiskos D. Malliaros, M. Vazirgiannis","doi":"10.1145/2433396.2433495","DOIUrl":"https://doi.org/10.1145/2433396.2433495","url":null,"abstract":"Graphs constitute a dominant data structure and appear essentially in all forms of information. Examples are the Web graph, numerous social networks, protein interaction networks, terms dependency graphs and network topologies. The main features of these graphs are their huge volume and rate of change. Presumably, there is important hidden knowledge in the macroscopic topology and features of these graphs. A cornerstone issue here is the detection and evaluation of communities -- bearing multiple and diverse semantics. The tutorial reports the basic models of graph structures for undirected, directed and signed graphs and their properties. Next we offer a thorough review of fundamental methods for graph clustering and community detection, on both undirected and directed graphs. Then we survey community evaluation measures, including both the individual node based ones as well as those that take into account aggregate properties of communities. A special mention is made on approaches that capitalize on the concept of degeneracy (k-cores and extensions), as a novel means of community detection and evaluation. We justify the above foundational framework with applications on citation graphs, trust networks and protein graphs.","PeriodicalId":324799,"journal":{"name":"Proceedings of the sixth ACM international conference on Web search and data mining","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123635546","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: 12

Workshop on semantic personalized information management (SPIM'13) 语义个性化信息管理研讨会(SPIM'13)

Proceedings of the sixth ACM international conference on Web search and data mining Pub Date : 2013-02-04 DOI: 10.1145/2433396.2433506

Till Plumbaum, E. W. D. Luca, Aldo Gangemi, M. Hausenblas

引用次数: 0

Retweet or not?: personalized tweet re-ranking 是否转发?:个性化的tweet重新排名

Proceedings of the sixth ACM international conference on Web search and data mining Pub Date : 2013-02-04 DOI: 10.1145/2433396.2433470

W. Feng, Jianyong Wang

{"title":"Retweet or not?: personalized tweet re-ranking","authors":"W. Feng, Jianyong Wang","doi":"10.1145/2433396.2433470","DOIUrl":"https://doi.org/10.1145/2433396.2433470","url":null,"abstract":"With Twitter being widely used around the world, users are facing enormous new tweets every day. Tweets are ranked in chronological order regardless of their potential interestedness. Users have to scan through pages of tweets to find useful information. Thus more personalized ranking scheme is needed to filter the overwhelmed information. Since retweet history reveals users' personal preference for tweets, we study how to learn a predictive model to rank the tweets according to their probability of being retweeted. In this way, users can find interesting tweets in a short time. To model the retweet behavior, we build a graph made up of three types of nodes: users, publishers and tweets. To incorporate all sources of information like users' profile, tweet quality, interaction history, etc, nodes and edges are represented by feature vectors. All these feature vectors are mapped to node weights and edge weights. Based on the graph, we propose a feature-aware factorization model to re-rank the tweets, which unifies the linear discriminative model and the low-rank factorization model seamlessly. Finally, we conducted extensive experiments on a real dataset crawled from Twitter. Experimental results show the effectiveness of our model.","PeriodicalId":324799,"journal":{"name":"Proceedings of the sixth ACM international conference on Web search and data mining","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127553072","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: 94

Exploiting social relations for sentiment analysis in microblogging 利用社交关系进行微博情感分析

Proceedings of the sixth ACM international conference on Web search and data mining Pub Date : 2013-02-04 DOI: 10.1145/2433396.2433465

Xia Hu, Lei Tang, Jiliang Tang, Huan Liu

{"title":"Exploiting social relations for sentiment analysis in microblogging","authors":"Xia Hu, Lei Tang, Jiliang Tang, Huan Liu","doi":"10.1145/2433396.2433465","DOIUrl":"https://doi.org/10.1145/2433396.2433465","url":null,"abstract":"Microblogging, like Twitter and Sina Weibo, has become a popular platform of human expressions, through which users can easily produce content on breaking news, public events, or products. The massive amount of microblogging data is a useful and timely source that carries mass sentiment and opinions on various topics. Existing sentiment analysis approaches often assume that texts are independent and identically distributed (i.i.d.), usually focusing on building a sophisticated feature space to handle noisy and short texts, without taking advantage of the fact that the microblogs are networked data. Inspired by the social sciences findings that sentiment consistency and emotional contagion are observed in social networks, we investigate whether social relations can help sentiment analysis by proposing a Sociological Approach to handling Noisy and short Texts (SANT) for sentiment classification. In particular, we present a mathematical optimization formulation that incorporates the sentiment consistency and emotional contagion theories into the supervised learning process; and utilize sparse learning to tackle noisy texts in microblogging. An empirical study of two real-world Twitter datasets shows the superior performance of our framework in handling noisy and short tweets.","PeriodicalId":324799,"journal":{"name":"Proceedings of the sixth ACM international conference on Web search and data mining","volume":"2022 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133013541","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: 392

Robust query rewriting using anchor data 使用锚数据进行健壮的查询重写

Proceedings of the sixth ACM international conference on Web search and data mining Pub Date : 2013-02-04 DOI: 10.1145/2433396.2433440

Nick Craswell, B. Billerbeck, Dennis Fetterly, Marc Najork

引用次数: 12

Sharding social networks 社交网络分片

Proceedings of the sixth ACM international conference on Web search and data mining Pub Date : 2013-02-04 DOI: 10.1145/2433396.2433424

Quang-huy Duong, Sharad Goel, J. Hofman, Sergei Vassilvitskii

引用次数: 29

Maguro, a system for indexing and searching over very large text collections 一个索引和搜索非常大的文本集合的系统

Proceedings of the sixth ACM international conference on Web search and data mining Pub Date : 2013-02-04 DOI: 10.1145/2433396.2433486

Knut Magne Risvik, Trishul M. Chilimbi, Henry Tan, Karthik Kalyanaraman, Chris Anderson

引用次数: 26