A graph models complex structural relationships among objects, and has been prevalently used in a wide range of applications. Building an automated graph classification model becomes very important for predicting unknown graphs or understanding complex structures between different classes. The graph classification framework being widely used consists of two steps, namely, feature selection and classification. The key issue is how to select important subgraph features from a graph database with a large number of graphs including positive graphs and negative graphs. Given the features selected, a generic classification approach can be used to build a classification model. In this paper, we focus on feature selection. We identify two main issues with the most widely used feature selection approach which is based on a discriminative score to select frequent subgraph features, and introduce a new diversified discriminative score to select features that have a higher diversity. We analyze the properties of the newly proposed diversified discriminative score, and conducted extensive performance studies to demonstrate that such a diversified discriminative score makes positive/negative graphs separable and leads to a higher classification accuracy.
{"title":"Graph classification: a diversified discriminative feature selection approach","authors":"Yuanyuan Zhu, J. Yu, Hong Cheng, Lu Qin","doi":"10.1145/2396761.2396791","DOIUrl":"https://doi.org/10.1145/2396761.2396791","url":null,"abstract":"A graph models complex structural relationships among objects, and has been prevalently used in a wide range of applications. Building an automated graph classification model becomes very important for predicting unknown graphs or understanding complex structures between different classes. The graph classification framework being widely used consists of two steps, namely, feature selection and classification. The key issue is how to select important subgraph features from a graph database with a large number of graphs including positive graphs and negative graphs. Given the features selected, a generic classification approach can be used to build a classification model. In this paper, we focus on feature selection. We identify two main issues with the most widely used feature selection approach which is based on a discriminative score to select frequent subgraph features, and introduce a new diversified discriminative score to select features that have a higher diversity. We analyze the properties of the newly proposed diversified discriminative score, and conducted extensive performance studies to demonstrate that such a diversified discriminative score makes positive/negative graphs separable and leads to a higher classification accuracy.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134235433","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}
Mihai Georgescu, Dang Duc Pham, C. S. Firan, W. Nejdl, Julien Gaugaz
Detecting duplicate entities, usually by examining metadata, has been the focus of much recent work. Several methods try to identify duplicate entities, while focusing either on accuracy or on efficiency and speed - with still no perfect solution. We propose a combined layered approach for duplicate detection with the main advantage of using Crowdsourcing as a training and feedback mechanism. By using Active Learning techniques on human provided examples, we fine tune our algorithm toward better duplicate detection accuracy. We keep the training cost low by gathering training data on demand for borderline cases or for inconclusive assessments. We apply our simple and powerful methods to an online publication search system: First, we perform a coarse duplicate detection relying on publication signatures in real time. Then, a second automatic step compares duplicate candidates and increases accuracy while adjusting based on both feedback from our online users and from Crowdsourcing platforms. Our approach shows an improvement of 14% over the untrained setting and is at only 4% difference to the human assessors in accuracy.
{"title":"Map to humans and reduce error: crowdsourcing for deduplication applied to digital libraries","authors":"Mihai Georgescu, Dang Duc Pham, C. S. Firan, W. Nejdl, Julien Gaugaz","doi":"10.1145/2396761.2398554","DOIUrl":"https://doi.org/10.1145/2396761.2398554","url":null,"abstract":"Detecting duplicate entities, usually by examining metadata, has been the focus of much recent work. Several methods try to identify duplicate entities, while focusing either on accuracy or on efficiency and speed - with still no perfect solution. We propose a combined layered approach for duplicate detection with the main advantage of using Crowdsourcing as a training and feedback mechanism. By using Active Learning techniques on human provided examples, we fine tune our algorithm toward better duplicate detection accuracy. We keep the training cost low by gathering training data on demand for borderline cases or for inconclusive assessments. We apply our simple and powerful methods to an online publication search system: First, we perform a coarse duplicate detection relying on publication signatures in real time. Then, a second automatic step compares duplicate candidates and increases accuracy while adjusting based on both feedback from our online users and from Crowdsourcing platforms. Our approach shows an improvement of 14% over the untrained setting and is at only 4% difference to the human assessors in accuracy.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134549942","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}
In this paper, we propose to query correlated graphs in a data stream scenario, where an algorithm is required to retrieve the top k graphs which are mostly correlated to a query graph q. Due to the dynamic changing nature of the stream data and the inherent complexity of the graph query process, treating graph streams as static datasets is computationally infeasible or ineffective. In the paper, we propose a novel algorithm, Hoe-PGPL, to identify top-k correlated graphs from data stream, by using a sliding window which covers a number of consecutive batches of stream data records. Our theme is to employ Hoeffding bound to discover some potential candidates and use two level candidate checking (one corresponding to the whole sliding window level and one corresponding to the local data batch level) to accurately estimate the correlation of the emerging candidate patterns, without rechecking the historical stream data. Experimental results demonstrate that the proposed algorithm not only achieves good performance in terms of query precision and recall, but also is several times, or even an order of magnitude, more efficient than the straightforward algorithm with respect to the time and the memory consumption. Our method represents the first research endeavor for data stream based top-k correlated graph query.
{"title":"Continuous top-k query for graph streams","authors":"Shirui Pan, Xingquan Zhu","doi":"10.1145/2396761.2398717","DOIUrl":"https://doi.org/10.1145/2396761.2398717","url":null,"abstract":"In this paper, we propose to query correlated graphs in a data stream scenario, where an algorithm is required to retrieve the top k graphs which are mostly correlated to a query graph q. Due to the dynamic changing nature of the stream data and the inherent complexity of the graph query process, treating graph streams as static datasets is computationally infeasible or ineffective. In the paper, we propose a novel algorithm, Hoe-PGPL, to identify top-k correlated graphs from data stream, by using a sliding window which covers a number of consecutive batches of stream data records. Our theme is to employ Hoeffding bound to discover some potential candidates and use two level candidate checking (one corresponding to the whole sliding window level and one corresponding to the local data batch level) to accurately estimate the correlation of the emerging candidate patterns, without rechecking the historical stream data. Experimental results demonstrate that the proposed algorithm not only achieves good performance in terms of query precision and recall, but also is several times, or even an order of magnitude, more efficient than the straightforward algorithm with respect to the time and the memory consumption. Our method represents the first research endeavor for data stream based top-k correlated graph query.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"142 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133970579","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}
In the TREC Web Diversity track, novelty-biased cumulative gain (α-NDCG) is one of the official measures to assess retrieval performance of IR systems. The measure is characterised by a parameter, α, the effect of which has not been thoroughly investigated. We find that common settings of α, i.e. α=0.5, may prevent the measure from behaving as desired when evaluating result diversification. This is because it excessively penalises systems that cover many intents while it rewards those that redundantly cover only few intents. This issue is crucial since it highly influences systems at top ranks. We revisit our previously proposed threshold, suggesting α be set on a query-basis. The intuitiveness of the measure is then studied by examining actual rankings from TREC 09-10 Web track submissions. By varying α according to our query-based threshold, the discriminative power of α-NDCG is not harmed and in fact, our approach improves α-NDCG's robustness. Experimental results show that the threshold for α can turn the measure to be more intuitive than using its common settings.
{"title":"A comprehensive analysis of parameter settings for novelty-biased cumulative gain","authors":"Teerapong Leelanupab, G. Zuccon, J. Jose","doi":"10.1145/2396761.2398550","DOIUrl":"https://doi.org/10.1145/2396761.2398550","url":null,"abstract":"In the TREC Web Diversity track, novelty-biased cumulative gain (α-NDCG) is one of the official measures to assess retrieval performance of IR systems. The measure is characterised by a parameter, α, the effect of which has not been thoroughly investigated. We find that common settings of α, i.e. α=0.5, may prevent the measure from behaving as desired when evaluating result diversification. This is because it excessively penalises systems that cover many intents while it rewards those that redundantly cover only few intents. This issue is crucial since it highly influences systems at top ranks. We revisit our previously proposed threshold, suggesting α be set on a query-basis. The intuitiveness of the measure is then studied by examining actual rankings from TREC 09-10 Web track submissions. By varying α according to our query-based threshold, the discriminative power of α-NDCG is not harmed and in fact, our approach improves α-NDCG's robustness. Experimental results show that the threshold for α can turn the measure to be more intuitive than using its common settings.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134105398","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}
In the online world, user engagement refers to the quality of the user experience that emphasizes the positive aspects of the interaction with a web application and, in particular, the phenomena associated with wanting to use that application longer and frequently. This definition is motivated by the observation that successful web applications are not just used, but they are engaged with. Users invest time, attention, and emotion into them. Online providers aim not only to engage users with each service, but across all services in their network. They spend increasing effort to direct users to various services (e.g.~using hyperlinks to help users navigate to and explore other services), to increase user traffic between their services. Nothing is known for users engaging across such a network of Web sites, something we call networked user engagement. We address this problem by combining techniques from web analytics and mining, information retrieval evaluation, and existing works on user engagement coming from the domains of information science, multimodal human computer interaction and cognitive psychology. In this way, we can combine insights from big data with deep analysis of human behavior in the lab or through crowd-sourcing experiments.
{"title":"User engagement: the network effect matters!","authors":"R. Baeza-Yates, M. Lalmas","doi":"10.1145/2396761.2396763","DOIUrl":"https://doi.org/10.1145/2396761.2396763","url":null,"abstract":"In the online world, user engagement refers to the quality of the user experience that emphasizes the positive aspects of the interaction with a web application and, in particular, the phenomena associated with wanting to use that application longer and frequently. This definition is motivated by the observation that successful web applications are not just used, but they are engaged with. Users invest time, attention, and emotion into them. Online providers aim not only to engage users with each service, but across all services in their network. They spend increasing effort to direct users to various services (e.g.~using hyperlinks to help users navigate to and explore other services), to increase user traffic between their services. Nothing is known for users engaging across such a network of Web sites, something we call networked user engagement. We address this problem by combining techniques from web analytics and mining, information retrieval evaluation, and existing works on user engagement coming from the domains of information science, multimodal human computer interaction and cognitive psychology. In this way, we can combine insights from big data with deep analysis of human behavior in the lab or through crowd-sourcing experiments.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131620022","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}
In this paper we present an approach for building a Wikipedia-based semantic network by integrating Open Information Extraction with Knowledge Acquisition techniques. Our algorithm extracts relation instances from Wikipedia page bodies and ontologizes them by, first, creating sets of synonymous relational phrases, called relation synsets, second, assigning semantic classes to the arguments of these relation synsets and, third, disambiguating the initial relation instances with relation synsets. As a result we obtain WiSeNet, a Wikipedia-based Semantic Network with Wikipedia pages as concepts and labeled, ontologized relations between them.
{"title":"WiSeNet: building a wikipedia-based semantic network with ontologized relations","authors":"A. Moro, Roberto Navigli","doi":"10.1145/2396761.2398495","DOIUrl":"https://doi.org/10.1145/2396761.2398495","url":null,"abstract":"In this paper we present an approach for building a Wikipedia-based semantic network by integrating Open Information Extraction with Knowledge Acquisition techniques. Our algorithm extracts relation instances from Wikipedia page bodies and ontologizes them by, first, creating sets of synonymous relational phrases, called relation synsets, second, assigning semantic classes to the arguments of these relation synsets and, third, disambiguating the initial relation instances with relation synsets. As a result we obtain WiSeNet, a Wikipedia-based Semantic Network with Wikipedia pages as concepts and labeled, ontologized relations between them.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133038044","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}
This paper proposes a new unified optimization framework combining pay-per-click auctions and guaranteed delivery in sponsored search. Advertisers usually have different (and sometimes mixed) marketing goals: brand awareness and direct response. Different mechanisms are good at addressing different goals, e.g., guaranteed delivery was often used to build brand awareness and pay-per-click auctions was widely used for direct marketing. Our new method accommodates both in a unified framework, with the search engine revenue as an optimization objective. In this way, we can target a guaranteed number of ad clicks (or impressions) per campaign for advertisers willing to pay a premium and enable keyword auctions for all others. Specifically, we formulate this joint optimization problem using linear programming and a column generation strategy for efficiency. To select the best column (a ranked list of ads) given a query, we propose a novel dynamic programming algorithm that takes the special structure of the ad allocation and pricing mechanisms into account. We have tested the proposed framework and the algorithms on real ad data obtained from a commercial search engine. The results demonstrate that our proposed approach can outperform several baselines in guaranteeing the number of clicks for the given advertisers, and in increasing the total revenue for the search engine.
{"title":"A unified optimization framework for auction and guaranteed delivery in online advertising","authors":"Konstantin Salomatin, Tie-Yan Liu, Yiming Yang","doi":"10.1145/2396761.2398561","DOIUrl":"https://doi.org/10.1145/2396761.2398561","url":null,"abstract":"This paper proposes a new unified optimization framework combining pay-per-click auctions and guaranteed delivery in sponsored search. Advertisers usually have different (and sometimes mixed) marketing goals: brand awareness and direct response. Different mechanisms are good at addressing different goals, e.g., guaranteed delivery was often used to build brand awareness and pay-per-click auctions was widely used for direct marketing. Our new method accommodates both in a unified framework, with the search engine revenue as an optimization objective. In this way, we can target a guaranteed number of ad clicks (or impressions) per campaign for advertisers willing to pay a premium and enable keyword auctions for all others. Specifically, we formulate this joint optimization problem using linear programming and a column generation strategy for efficiency. To select the best column (a ranked list of ads) given a query, we propose a novel dynamic programming algorithm that takes the special structure of the ad allocation and pricing mechanisms into account. We have tested the proposed framework and the algorithms on real ad data obtained from a commercial search engine. The results demonstrate that our proposed approach can outperform several baselines in guaranteeing the number of clicks for the given advertisers, and in increasing the total revenue for the search engine.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133045083","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}
Interleaved comparison methods, which compare rankers using click data, are a promising alternative to traditional information retrieval evaluation methods that require expensive explicit judgments. A major limitation of these methods is that they assume access to live data, meaning that new data must be collected for every pair of rankers compared. We investigate the use of previously collected click data (i.e., historical data) for interleaved comparisons. We start by analyzing to what degree existing interleaved comparison methods can be applied and find that a recent probabilistic method allows such data reuse, even though it is biased when applied to historical data. We then propose an interleaved comparison method that is based on the probabilistic approach but uses importance sampling to compensate for bias. We experimentally confirm that probabilistic methods make the use of historical data for interleaved comparisons possible and effective.
{"title":"Estimating interleaved comparison outcomes from historical click data","authors":"Katja Hofmann, Shimon Whiteson, M. de Rijke","doi":"10.1145/2396761.2398516","DOIUrl":"https://doi.org/10.1145/2396761.2398516","url":null,"abstract":"Interleaved comparison methods, which compare rankers using click data, are a promising alternative to traditional information retrieval evaluation methods that require expensive explicit judgments. A major limitation of these methods is that they assume access to live data, meaning that new data must be collected for every pair of rankers compared. We investigate the use of previously collected click data (i.e., historical data) for interleaved comparisons. We start by analyzing to what degree existing interleaved comparison methods can be applied and find that a recent probabilistic method allows such data reuse, even though it is biased when applied to historical data. We then propose an interleaved comparison method that is based on the probabilistic approach but uses importance sampling to compensate for bias. We experimentally confirm that probabilistic methods make the use of historical data for interleaved comparisons possible and effective.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133465180","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}
Focusing on the top-K items according to a ranking criterion constitutes an important functionality in many different query answering scenarios. The idea is to read only the necessary information---mostly from secondary storage---with the ultimate goal to achieve low latency. In this work, we consider processing such top-K queries under the constraint that the result items are members of a specific set, which is provided at query time. We call this restriction a set-defined selection criterion. Set-defined selections drastically influence the pros and cons of an id-ordered index vs. a score-ordered index. We present a mathematical model that allows to decide at runtime which index to choose, leading to a combined index. To improve the latency around the break even point of the two indices, we show how to benefit from a partitioned score-ordered index and present an algorithm to create such partitions based on analyzing query logs. Further performance gains can be enjoyed using approximate top-K results, with tunable result quality. The presented approaches are evaluated using both real-world and synthetic data.
{"title":"Being picky: processing top-k queries with set-defined selections","authors":"A. Stupar, S. Michel","doi":"10.1145/2396761.2396877","DOIUrl":"https://doi.org/10.1145/2396761.2396877","url":null,"abstract":"Focusing on the top-K items according to a ranking criterion constitutes an important functionality in many different query answering scenarios. The idea is to read only the necessary information---mostly from secondary storage---with the ultimate goal to achieve low latency. In this work, we consider processing such top-K queries under the constraint that the result items are members of a specific set, which is provided at query time. We call this restriction a set-defined selection criterion. Set-defined selections drastically influence the pros and cons of an id-ordered index vs. a score-ordered index. We present a mathematical model that allows to decide at runtime which index to choose, leading to a combined index. To improve the latency around the break even point of the two indices, we show how to benefit from a partitioned score-ordered index and present an algorithm to create such partitions based on analyzing query logs. Further performance gains can be enjoyed using approximate top-K results, with tunable result quality. The presented approaches are evaluated using both real-world and synthetic data.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"326 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132663474","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}
Finding the minimum connected subtree of a graph that contains a given set of nodes (i.e., the Steiner tree problem) is a fundamental operation in keyword search in graphs, yet it is known to be NP-hard. Existing approximation techniques either make use of the heavy indexing of the graph, or entirely rely on online heuristics. In this paper we bridge the gap between these two extremes and present a scalable landmark-based index structure that, combined with a few lightweight online heuristics, yields a fast and accurate approximation of the Steiner tree. Our solution handles real-world graphs with millions of nodes and provides an approximation error of less than 5% on average.
{"title":"Fast approximation of steiner trees in large graphs","authors":"Andrey Gubichev, Thomas Neumann","doi":"10.1145/2396761.2398460","DOIUrl":"https://doi.org/10.1145/2396761.2398460","url":null,"abstract":"Finding the minimum connected subtree of a graph that contains a given set of nodes (i.e., the Steiner tree problem) is a fundamental operation in keyword search in graphs, yet it is known to be NP-hard. Existing approximation techniques either make use of the heavy indexing of the graph, or entirely rely on online heuristics. In this paper we bridge the gap between these two extremes and present a scalable landmark-based index structure that, combined with a few lightweight online heuristics, yields a fast and accurate approximation of the Steiner tree. Our solution handles real-world graphs with millions of nodes and provides an approximation error of less than 5% on average.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132709750","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}
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