Proceedings of the 18th ACM conference on Information and knowledge management最新文献_第9页

A scalable and effective full-text search in P2P networks 在P2P网络中一个可扩展和有效的全文搜索

Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646281

Y. Mass, Y. Sagiv, Michal Shmueli-Scheuer

引用次数: 5

ExSearch: a novel vertical search engine for online barter business ExSearch:一个新颖的垂直搜索引擎，用于在线易货业务

Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646125

Lei Ji, Jun Yan, Ning Liu, Wen Zhang, Weiguo Fan, Zheng Chen

{"title":"ExSearch: a novel vertical search engine for online barter business","authors":"Lei Ji, Jun Yan, Ning Liu, Wen Zhang, Weiguo Fan, Zheng Chen","doi":"10.1145/1645953.1646125","DOIUrl":"https://doi.org/10.1145/1645953.1646125","url":null,"abstract":"E-Commerce has shown its exponentially-growing business value in the past decade. However, in contrast to the successful examples in online sales, such as Amazon1 and eBay2, the online barter business is still underexplored due to the lack of corresponding information aggregation service. In this paper, we design and implement a novel vertical search engine, called ExSearch, to aggregate online barter information for developing the barter market. Different from classical general purpose Web search engines, ExSearch adopts a focused crawler to gather related information from various websites. We propose to automatically extract the barter information from free-text Web pages such that the unstructured information is represented in structured databases. In addition, we utilize the data mining techniques such as regression to fulfill the missing information, which cannot be extracted from the Web pages. Finally, we validate and rank the search results according to user queries. Experimental results show that each component module in our proposed ExSearch system is efficient and effective. The volunteer users are satisfied by and interested in this novel vertical search engine.","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125344336","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

Using domain ontology for semantic web usage mining and next page prediction 利用领域本体进行语义web使用挖掘和下一页预测

Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646202

Nizar R. Mabroukeh, C. Ezeife

引用次数: 43

Pseudo relevance feedback using semantic clustering in relevance language model 关联语言模型中基于语义聚类的伪关联反馈

Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646268

Qiang Pu, Daqing He

{"title":"Pseudo relevance feedback using semantic clustering in relevance language model","authors":"Qiang Pu, Daqing He","doi":"10.1145/1645953.1646268","DOIUrl":"https://doi.org/10.1145/1645953.1646268","url":null,"abstract":"Pseudo relevance feedback has demonstrated to be in general an effective technique for improving retrieval effectiveness, but the noise in the top retrieved documents still can cause topic drift problem that affects the performance of certain topics. By viewing a document as an interaction of a set of independent hidden topics, we propose a novel semantic clustering technique using independent component analysis. Then within the language modeling framework, we apply the obtained semantic topic clusters into the query sampling process so that the sampling depends on the activated topics rather than on the individual document language model. Therefore, we obtain a semantic cluster based relevance language model, which uses pseudo relevance feedback technique without requiring any relevance training information. We applied the model on five TREC data sets. The experiments show that our model can significantly improve retrieval performance over traditional language models including relevance-based and clustering-based retrieval language models. The main contribution of the improvements comes from the estimation of the relevance model on the semantic clusters that are closely related to the query.","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128862035","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: 3

Who tags the tags?: a framework for bookmark weighting 谁给标签贴上标签?:书签权重的框架

Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646176

David Carmel, Haggai Roitman, E. Yom-Tov

引用次数: 12

Demonstration of an RFID middleware: LIT ALE manager 一个RFID中间件的演示:LIT ALE管理器

Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646306

Qiang Wang, W. Ryu, Soohan Kim, B. Hong

引用次数: 6

Blog cascade affinity: analysis and prediction 博客级联亲和力:分析与预测

Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646095

Hui Li, S. Bhowmick, Aixin Sun

引用次数: 14

What happens after an ad click?: quantifying the impact of landing pages in web advertising 广告点击后会发生什么?:量化登陆页在网络广告中的影响

Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1645964

H. Becker, A. Broder, E. Gabrilovich, V. Josifovski, B. Pang

{"title":"What happens after an ad click?: quantifying the impact of landing pages in web advertising","authors":"H. Becker, A. Broder, E. Gabrilovich, V. Josifovski, B. Pang","doi":"10.1145/1645953.1645964","DOIUrl":"https://doi.org/10.1145/1645953.1645964","url":null,"abstract":"Unbeknownst to most users, when a query is submitted to a search engine two distinct searches are performed: the organic or algorithmic search that returns relevant Web pages and related data (maps, images, etc.), and the sponsored search that returns paid advertisements. While an enormous amount of work has been invested in understanding the user interaction with organic search, surprisingly little research has been dedicated to what happens after an ad is clicked, a situation we aim to correct. To this end, we define and study the process of context transfer, that is, the user's transition from Web search to the context of the landing page that follows an ad-click. We conclude that in the vast majority of cases the user is shown one of three types of pages, namely, Homepage (the homepage of the advertiser), Category browse (a browse-able sub-catalog related to the original query), and Search transfer (the search results of the same query re-executed on the target site). We show that these three types of landing pages can be accurately distinguished using automatic text classification. Finally, using such an automatic classifier, we correlate the landing page type with conversion data provided by advertisers, and show that the conversion rate (i.e., users' response rate to ads) varies considerably according to the type. We believe our findings will further the understanding of users' response to search advertising in general, and landing pages in particular, and thus help advertisers improve their Web sites and help search engines select the most suitable ads.","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125483310","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: 25

A comparative study of methods for estimating query language models with pseudo feedback 基于伪反馈的查询语言模型估计方法的比较研究

Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646259

Yuanhua Lv, ChengXiang Zhai

引用次数: 196

Scalable learning of collective behavior based on sparse social dimensions 基于稀疏社会维度的集体行为可扩展学习

Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646094

Lei Tang, Huan Liu

{"title":"Scalable learning of collective behavior based on sparse social dimensions","authors":"Lei Tang, Huan Liu","doi":"10.1145/1645953.1646094","DOIUrl":"https://doi.org/10.1145/1645953.1646094","url":null,"abstract":"The study of collective behavior is to understand how individuals behave in a social network environment. Oceans of data generated by social media like Facebook, Twitter, Flickr and YouTube present opportunities and challenges to studying collective behavior in a large scale. In this work, we aim to learn to predict collective behavior in social media. In particular, given information about some individuals, how can we infer the behavior of unobserved individuals in the same network? A social-dimension based approach is adopted to address the heterogeneity of connections presented in social media. However, the networks in social media are normally of colossal size, involving hundreds of thousands or even millions of actors. The scale of networks entails scalable learning of models for collective behavior prediction. To address the scalability issue, we propose an edge-centric clustering scheme to extract sparse social dimensions. With sparse social dimensions, the social-dimension based approach can efficiently handle networks of millions of actors while demonstrating comparable prediction performance as other non-scalable methods.","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117211627","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: 253