Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management最新文献

{"title":"On bias problem in relevance feedback","authors":"Qianli Xing, Yi Zhang, Lanbo Zhang","doi":"10.1145/2063576.2063866","DOIUrl":"https://doi.org/10.1145/2063576.2063866","url":null,"abstract":"Relevance feedback is an effective approach to improve retrieval quality over the initial query. Typical relevance feedback methods usually select top-ranked documents for relevance judgments, then query expansion or model updating are carried out based on the feedback documents. However, the number of feedback documents is usually limited due to expensive human labeling. Thus relevant documents in the feedback set are hardly representative of all relevant documents and the feedback set is actually biased. As a result, the performance of relevance feedback will get hurt. In this paper, we first show how and where the bias problem exists through experiments. Then we study how the bias can be reduced by utilizing the unlabeled documents. After analyzing the usefulness of a document to relevance feedback, we propose an approach that extends the feedback set with carefully selected unlabeled documents by heuristics. Our experiment results show that the extended feedback set has less bias than the original feedback set and better performance can be achieved when the extended feedback set is used for relevance feedback.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"15 1","pages":"1965-1968"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89579408","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}

{"title":"Exploiting longer cycles for link prediction in signed networks","authors":"Kai-Yang Chiang, Nagarajan Natarajan, Ambuj Tewari, I. Dhillon","doi":"10.1145/2063576.2063742","DOIUrl":"https://doi.org/10.1145/2063576.2063742","url":null,"abstract":"We consider the problem of link prediction in signed networks. Such networks arise on the web in a variety of ways when users can implicitly or explicitly tag their relationship with other users as positive or negative. The signed links thus created reflect social attitudes of the users towards each other in terms of friendship or trust. Our first contribution is to show how any quantitative measure of social imbalance in a network can be used to derive a link prediction algorithm. Our framework allows us to reinterpret some existing algorithms as well as derive new ones. Second, we extend the approach of Leskovec et al. (2010) by presenting a supervised machine learning based link prediction method that uses features derived from longer cycles in the network. The supervised method outperforms all previous approaches on 3 networks drawn from sources such as Epinions, Slashdot and Wikipedia. The supervised approach easily scales to these networks, the largest of which has 132k nodes and 841k edges. Most real-world networks have an overwhelmingly large proportion of positive edges and it is therefore easy to get a high overall accuracy at the cost of a high false positive rate. We see that our supervised method not only achieves good accuracy for sign prediction but is also especially effective in lowering the false positive rate.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"51 1","pages":"1157-1162"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89417575","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}

{"title":"Learning kernels with upper bounds of leave-one-out error","authors":"Yong Liu, Shizhong Liao, Yuexian Hou","doi":"10.1145/2063576.2063927","DOIUrl":"https://doi.org/10.1145/2063576.2063927","url":null,"abstract":"We propose a new leaning method for Multiple Kernel Learning (MKL) based on the upper bounds of the leave-one-out error that is an almost unbiased estimate of the expected generalization error. Specifically, we first present two new formulations for MKL by minimizing the upper bounds of the leave-one-out error. Then, we compute the derivatives of these bounds and design an efficient iterative algorithm for solving these formulations. Experimental results show that the proposed method gives better accuracy results than that of both SVM with the uniform combination of basis kernels and other state-of-art kernel learning approaches.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"149 1","pages":"2205-2208"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76751948","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}

{"title":"DTMBIO 2011: international workshop on data and textmining in biomedical informatics","authors":"S. Ananiadou, Doheon Lee, S. Navathe, Min-Keun Song","doi":"10.1145/2063576.2064041","DOIUrl":"https://doi.org/10.1145/2063576.2064041","url":null,"abstract":"ACM Fifth International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO 11) organizers are pleased to announce that the fifth DTMBIO will be held in conjunction with CIKM, one of the largest data and text mining conferences. While CIKM presents the state-of-the-art research in informatics with the primary focus on data and text mining, the main focus of DTMBIO is on biomedical informatics. DTMBIO delegates will bring forth interesting applications of up-to-date informatics in the context of biomedical research.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"13 1","pages":"2617-2618"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78502759","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}

{"title":"A framework for personalized and collaborative clustering of search results","authors":"D. Anastasiu, Byron J. Gao, David J. Buttler","doi":"10.1145/2063576.2063662","DOIUrl":"https://doi.org/10.1145/2063576.2063662","url":null,"abstract":"How to organize and present search results plays a critical role in the utility of search engines. Due to the unprecedented scale of the Web and diversity of search results, the common strategy of ranked lists has become increasingly inadequate, and clustering has been considered as a promising alternative. Clustering divides a long list of disparate search results into a few topic-coherent clusters, allowing the user to quickly locate relevant results by topic navigation. While many clustering algorithms have been proposed that innovate on the automatic clustering procedure, we introduce ClusteringWiki, the first prototype and framework for personalized clustering that allows direct user editing of the clustering results. Through a Wiki interface, the user can edit and annotate the membership, structure and labels of clusters for a personalized presentation. In addition, the edits and annotations can be shared among users as a mass-collaborative way of improving search result organization and search engine utility.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"12 1","pages":"573-582"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78505434","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}

{"title":"Designing an ensemble classifier over subspace classifiers using iterative convergence routine","authors":"B. Vinzamuri, K. Karlapalem","doi":"10.1145/2063576.2063678","DOIUrl":"https://doi.org/10.1145/2063576.2063678","url":null,"abstract":"There can be multiple classifiers for a given data set. One way to generate multiple classifiers is to use subspaces of the attribute sets. In this paper, we generate subspace classifiers by an iterative convergence routine to build an ensemble classifier. Experimental evaluation covers the cases of both labelled and unlabelled (blind) data separately. We evaluate our approach on many benchmark UC Irvine datasets to assess the robustness of our approach with varying induced noise levels. We explicitly compare and present the utility of the clusterings generated for classification using several diverse clustering dissimilarity metrics. Results show that our ensemble classifier is a more robust classifier in comparison to different multi-class classification approaches.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"1 1","pages":"693-698"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77994958","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}

{"title":"Utility-driven anonymization in data publishing","authors":"Mingqiang Xue, Panagiotis Karras, Chedy Raïssi, H. Pung","doi":"10.1145/2063576.2063945","DOIUrl":"https://doi.org/10.1145/2063576.2063945","url":null,"abstract":"Privacy-preserving data publication has been studied intensely in the past years. Still, all existing approaches transform data values by random perturbation or generalization. In this paper, we introduce a radically different data anonymization methodology. Our proposal aims to maintain a certain amount of patterns, defined in terms of a set of properties of interest that hold for the original data. Such properties are represented as linear relationships among data points. We present an algorithm that generates a set of anonymized data that strictly preserves these properties, thus maintaining specified patterns in the data. Extensive experiments with real and synthetic data show that our algorithm is efficient, and produces anonymized data that affords high utility in several data analysis tasks while safeguarding privacy.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"1 1","pages":"2277-2280"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76273776","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}

{"title":"Discovering trending phrases on information streams","authors":"K. Kamath, James Caverlee","doi":"10.1145/2063576.2063937","DOIUrl":"https://doi.org/10.1145/2063576.2063937","url":null,"abstract":"We study the problem of efficient discovery of trending phrases from high-volume text streams -- be they sequences of Twitter messages, email messages, news articles, or other time-stamped text documents. Most existing approaches return top-k trending phrases. But, this approach neither guarantees that the top-k phrases returned are all trending, nor that all trending phrases are returned. In addition, the value of k is difficult to set and is indifferent to stream dynamics. Hence, we propose an approach that identifies all the trending phrases in a stream and is flexible to the changing stream properties.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"23 1","pages":"2245-2248"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73144037","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}

{"title":"Social and collaborative information seeking: panel","authors":"Jeremy Pickens","doi":"10.1145/2063576.2064057","DOIUrl":"https://doi.org/10.1145/2063576.2064057","url":null,"abstract":"In recent years, information retrieval and information seeking have moved beyond their single-user roots and are becoming multi-user endeavors. However, there are multiple visions for how best to design multi-user interactions: social search versus collaborative search. The terms \"social\" and \"collaborative\" are overloaded with meaning, having been used to describe a wide variety of systems, user needs and goals, interaction styles, and algorithms. In this panel we adopt the following primary definitions: Information seeking tasks in which there are two or more people who lack the same information (share the same information need) and explicitly set out together to satisfy that need are known as collaborative. A collaborative information retrieval system provides mechanisms -- interfaces and mediation algorithms -- that allow the team to work together to find information that neither individual would have found when working alone. There is an inherent division of labor in collaborative work.\u0000 On the other hand, information seeking tasks in which only a single individual lacks information, but is willing or able to let an larger group assist in the satisfaction of that need, is known as social search. The larger group may be an community of like-minded individuals, or it might be a social network of friends and associates. But either way, the assumption is that someone in that community or network already possesses the information that the initial individual seeks. The goal of the system is therefore to correctly propagate or diffuse that existing knowledge throughout the network, to amplify and repeat information that has already been discovered by at least one person.\u0000 Despite these fundamental differences between collaborative (team-oriented, jointly-held information need) and social (network- and community-augmented, though ultimately solitary need), there are similarities in process. This panel will explore both these similarities and differences, and provide insight about whether one type of multi-user information seeking vision will ultimately eclipse the other, or whether each will remain separate but complementary.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"50 1","pages":"2647-2648"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74440072","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}

{"title":"TAKES: a fast method to select features in the kernel space","authors":"Ye Xu, S. Furao, Wei Ping, Jinxi Zhao","doi":"10.1145/2063576.2063677","DOIUrl":"https://doi.org/10.1145/2063576.2063677","url":null,"abstract":"Feature selection is an effective tool to deal with the \"curse of dimensionality\". To cope with the non-separable problem, feature selection in the kernel space has been investigated. However, previous study cannot adequately estimate the intrinsic dimensionality of the kernel space. Thus, it is difficult to accurately preserve the sketch of the kernel space using the learned basis, and the feature selection performance is affected. Moreover, the computing load of the algorithm reaches at least cubic with the number of training data. In this paper, we propose a fast framework to conduct feature selection in the kernel space. By designing a fast kernel subspace learning method, we automatically learn the intrinsic dimensionality and construct an orthogonal basis set of kernel space. The learned basis can accurately preserve the sketch of kernel space. Then backed by the constructed basis, we directly select features in kernel space. The whole proposed framework has a quadratic complexity with the number of training data, which is faster than existing kernel methods for feature selection. We evaluate our work under several typical datasets and find it not only preserves the sketch of the kernel space more accurately but also achieves better classification performance compared with many state-of-the-art methods.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"163 1","pages":"683-692"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72788581","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}