Proceedings of the 19th ACM international conference on Information and knowledge management最新文献_第2页

Online learning for recency search ranking using real-time user feedback 使用实时用户反馈进行最近搜索排名的在线学习

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871657

Taesup Moon, Lihong Li, Wei Chu, Ciya Liao, Zhaohui Zheng, Yi Chang

{"title":"Online learning for recency search ranking using real-time user feedback","authors":"Taesup Moon, Lihong Li, Wei Chu, Ciya Liao, Zhaohui Zheng, Yi Chang","doi":"10.1145/1871437.1871657","DOIUrl":"https://doi.org/10.1145/1871437.1871657","url":null,"abstract":"Traditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance of documents for a given query. Although such a batch-learning framework has been tremendously successful in commercial search engines, in scenarios where relevance of documents to a query changes over time, such as ranking recent documents for a breaking news query, the batch-learned ranking functions do have limitations. Users' real-time click feedback becomes a better and timely proxy for the varying relevance of documents rather than the editorial judgments provided by human editors. In this paper, we propose an online learning algorithm that can quickly learn the best re-ranking of the top portion of the original ranked list based on real-time users' click feedback. In order to devise our algorithm and evaluate it accurately, we collected exploration bucket data that removes positional biases on clicks on the documents for recency-classified queries. Our initial experimental result shows that our scheme is more capable of quickly adjusting the ranking to track the varying relevance of documents reflected in the click feedback, compared to batch-trained ranking functions.","PeriodicalId":310611,"journal":{"name":"Proceedings of the 19th ACM international conference on Information and knowledge management","volume":"800 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117041798","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

Partial drift detection using a rule induction framework 部分漂移检测使用规则归纳框架

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871536

Damon Sotoudeh, Aijun An

{"title":"Partial drift detection using a rule induction framework","authors":"Damon Sotoudeh, Aijun An","doi":"10.1145/1871437.1871536","DOIUrl":"https://doi.org/10.1145/1871437.1871536","url":null,"abstract":"The major challenge in mining data streams is the issue of concept drift, the tendency of the underlying data generation process to change over time. In this paper, we propose a general rule learning framework that can efficiently handle concept-drifting data streams and maintain a highly accurate classification model. The main idea is to focus on partial drifts by allowing individual rules to monitor the stream and detect if there is a drift in the regions they cover. A rule quality measure then decides whether the affected rules are inconsistent with the concept drift. The model is accordingly updated to only include rules that are consistent with the newly arrived concept. A dynamically maintained set of instances deemed relevant to the most recent concept is also kept at memory. Learning a new concept from a larger set of instances reduces the variance of data distribution and allows for a more accurate, stable classification model. Our experiments show that this approach not only handles the drift efficiently, but it also can provide higher classification accuracy compared to other competitive approaches on a variety of real and synthetic data sets.","PeriodicalId":310611,"journal":{"name":"Proceedings of the 19th ACM international conference on Information and knowledge management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128755041","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

Top-Eye: top-k evolving trajectory outlier detection Top-Eye: top-k进化轨迹异常点检测

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871716

Yong Ge, Hui Xiong, Zhi-Hua Zhou, H. Ozdemir, Jannite Yu, Kuo Chu Lee

{"title":"Top-Eye: top-k evolving trajectory outlier detection","authors":"Yong Ge, Hui Xiong, Zhi-Hua Zhou, H. Ozdemir, Jannite Yu, Kuo Chu Lee","doi":"10.1145/1871437.1871716","DOIUrl":"https://doi.org/10.1145/1871437.1871716","url":null,"abstract":"The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for identifying abnormal moving activities. Indeed, various aspects of abnormality of moving patterns have recently been exploited, such as wrong direction and wandering. However, there is no recognized way of combining different aspects into an unified evolving abnormality score which has the ability to capture the evolving nature of abnormal moving trajectories. To that end, in this paper, we provide an evolving trajectory outlier detection method, named TOP-EYE, which continuously computes the outlying score for each trajectory in an accumulating way. Specifically, in TOP-EYE, we introduce a decay function to mitigate the influence of the past trajectories on the evolving outlying score, which is defined based on the evolving moving direction and density of trajectories. This decay function enables the evolving computation of accumulated outlying scores along the trajectories. An advantage of TOP-EYE is to identify evolving outliers at very early stage with relatively low false alarm rate. Finally, experimental results on real-world location traces show that TOP-EYE can effectively capture evolving abnormal trajectories.","PeriodicalId":310611,"journal":{"name":"Proceedings of the 19th ACM international conference on Information and knowledge management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128259609","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: 126

Learning a user-thread alignment manifold for thread recommendation in online forum 学习用户-线程对齐歧管，用于在线论坛的线程推荐

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871511

Jun Zhao, Jiajun Bu, Chun Chen, Ziyu Guan, C. Wang, Cheng Zhang

{"title":"Learning a user-thread alignment manifold for thread recommendation in online forum","authors":"Jun Zhao, Jiajun Bu, Chun Chen, Ziyu Guan, C. Wang, Cheng Zhang","doi":"10.1145/1871437.1871511","DOIUrl":"https://doi.org/10.1145/1871437.1871511","url":null,"abstract":"People are more and more willing to participate in online forums to share their knowledge and experience. However, it may not be easy for them to find their desired threads in online forums due to the information overload problem. Traditional recommendation approaches can not be directly applied to online forums due to two reasons. First, unlike traditional movie or music recommendation problem, there is no rating information in online forums. Second, the sparsity problem is more severe since the users may only read threads but take no actions. To address these limitations, in this paper we propose to make use of the reply relationships among users, as well as thread contents. A learning algorithm is introduced to infer a user-thread alignment manifold in which both users and thread contents can be well represented. Thus, the relatedness between users and threads can be measured on this alignment manifold, and the closest threads which can best meet the corresponding user's information needs are recommended. Experiments on a dataset crawled from digg.com have demonstrated the superiority of our algorithm over traditional recommendation algorithms.","PeriodicalId":310611,"journal":{"name":"Proceedings of the 19th ACM international conference on Information and knowledge management","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129467961","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

OpinionIt: a text mining system for cross-lingual opinion analysis OpinionIt:一个用于跨语言意见分析的文本挖掘系统

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871589

Honglei Guo, Huijia Zhu, Zhili Guo, Xiaoxun Zhang, Zhong Su

{"title":"OpinionIt: a text mining system for cross-lingual opinion analysis","authors":"Honglei Guo, Huijia Zhu, Zhili Guo, Xiaoxun Zhang, Zhong Su","doi":"10.1145/1871437.1871589","DOIUrl":"https://doi.org/10.1145/1871437.1871589","url":null,"abstract":"Opinion mining focuses on extracting customers' opinions from the reviews and predicting their sentiment orientation. Reviewers usually praise a product in some aspects and bemoan it in other aspects. With the business globalization, it is very important for enterprises to extract the opinions toward different aspects and find out cross-lingual/cross-culture difference in opinions. Cross-lingual opinion mining is a very challenging task as amounts of opinions are written in different languages, and not well structured. Since people usually use different words to describe the same aspect in the reviews, product-feature (PF) categorization becomes very critical in cross-lingual opinion mining. Manual cross-lingual PF categorization is time consuming, and practically infeasible for the massive amount of data written in different languages. In order to effectively find out cross-lingual difference in opinions, we present an aspect-oriented opinion mining method with Cross-lingual Latent Semantic Association (CLaSA). We first construct CLaSA model to learn the cross-lingual latent semantic association among all the PFs from multi-dimension semantic clues in the review corpus. Then we employ CLaSA model to categorize all the multilingual PFs into semantic aspects, and summarize cross-lingual difference in opinions towards different aspects. Experimental results show that our method achieves better performance compared with the existing approaches. With CLaSA model, our text mining system OpinionIt can effectively discover cross-lingual difference in opinions.","PeriodicalId":310611,"journal":{"name":"Proceedings of the 19th ACM international conference on Information and knowledge management","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130589433","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: 28

Probabilistic first pass retrieval for search advertising: from theory to practice 搜索广告的概率首遍检索:从理论到实践

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871567

Hema Raghavan, R. Iyer

{"title":"Probabilistic first pass retrieval for search advertising: from theory to practice","authors":"Hema Raghavan, R. Iyer","doi":"10.1145/1871437.1871567","DOIUrl":"https://doi.org/10.1145/1871437.1871567","url":null,"abstract":"Information retrieval in search advertising, as in other ad-hoc retrieval tasks, aims to find the most appropriate ranking of the ad documents of a corpus for a given query. In addition to ranking the ad documents, we also need to filter or threshold irrelevant ads from participating in the auction to be displayed alongside search results. In this work, we describe our experience in implementing a successful ad retrieval system for a commercial search engine based on the Language Modeling (LM) framework for retrieval. The LM demonstrates significant performance improvements over the baseline vector space model (TF-IDF) system that was in production at the time. From a modeling perspective, we propose a novel approach to incorporate query segmentation and phrases in the LM framework, discuss impact of score normalization for relevance filtering, and present preliminary results of incorporating query expansions using query rewriting techniques. From an implementation perspective, we also discuss real-time latency constraints of a production search engine and how we overcome them by adapting the WAND algorithm to work with language models. In sum, our LM formulation is considerably better in terms of accuracy metrics such as Precision-Recall (10% improvement in AUC) and nDCG (8% improvement in nDCG@5) on editorial data and also demonstrates significant improvements in clicks in live user tests (0.787% improvement in Click Yield, with 8% coverage increase). Finally, we hope that this paper provides the reader with adequate insights into the challenges of building a system that serves millions of users every day.","PeriodicalId":310611,"journal":{"name":"Proceedings of the 19th ACM international conference on Information and knowledge management","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121317118","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: 11

Opinion digger: an unsupervised opinion miner from unstructured product reviews 意见挖掘者:从非结构化的产品评论中挖掘无监督的意见

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871739

Samaneh Moghaddam, M. Ester

引用次数: 209

MI-WDIS: web data integration system for market intelligence MI-WDIS:用于市场情报的web数据集成系统

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871783

Zhongmin Yan, Qingzhong Li, Shidong Zhang, Zhaohui Peng, Yongquan Dong, Yanhui Ding, Yongxin Zhang, Xiuxing Xu

引用次数: 4

Learning naïve bayes transfer classifier throughclass-wise test distribution estimation 学习naïve贝叶斯转移分类器通过类明智的测试分布估计

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871715

J. Son, Seong-Bae Park, Hyun-Je Song

引用次数: 1

Selective data acquisition for probabilistic K-NN query 概率K-NN查询的选择性数据采集

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871620

Yu-Chieh Lin, De-Nian Yang, Ming-Syan Chen

引用次数: 1