Existing recommender systems model user interests and the social influences independently. In reality, user interests may change over time, and as the interests change, new friends may be added while old friends grow apart and the new friendships formed may cause further interests change. This complex interaction requires the joint modeling of user interest and social relationships over time. In this paper, we propose a probabilistic generative model, called Receptiveness over Time Model (RTM), to capture this interaction. We design a Gibbs sampling algorithm to learn the receptiveness and interest distributions among users over time. The results of experiments on a real world dataset demonstrate that RTM-based recommendation outperforms the state-of-the-art recommendation methods. Case studies also show that RTM is able to discover the user interest shift and receptiveness change over time
{"title":"Modeling user's receptiveness over time for recommendation","authors":"Wei Chen, W. Hsu, M. Lee","doi":"10.1145/2484028.2484047","DOIUrl":"https://doi.org/10.1145/2484028.2484047","url":null,"abstract":"Existing recommender systems model user interests and the social influences independently. In reality, user interests may change over time, and as the interests change, new friends may be added while old friends grow apart and the new friendships formed may cause further interests change. This complex interaction requires the joint modeling of user interest and social relationships over time. In this paper, we propose a probabilistic generative model, called Receptiveness over Time Model (RTM), to capture this interaction. We design a Gibbs sampling algorithm to learn the receptiveness and interest distributions among users over time. The results of experiments on a real world dataset demonstrate that RTM-based recommendation outperforms the state-of-the-art recommendation methods. Case studies also show that RTM is able to discover the user interest shift and receptiveness change over time","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121751108","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}
When generating query recommendations for a user, a natural approach is to try and leverage not only the user's most recently submitted query, or reference query, but also information about the current search context, such as the user's recent search interactions. We focus on two important classes of queries that make up search contexts: those that address the same information need as the reference query (on-task queries), and those that do not (off-task queries). We analyze the effects on query recommendation performance of using contexts consisting of only on-task queries, only off-task queries, and a mix of the two. Using TREC Session Track data for simulations, we demonstrate that on-task context is helpful on average but can be easily overwhelmed when off-task queries are interleaved---a common situation according to several analyses of commercial search logs. To minimize the impact of off-task queries on recommendation performance, we consider automatic methods of identifying such queries using a state of the art search task identification technique. Our experimental results show that automatic search task identification can eliminate the effect of off-task queries in a mixed context. We also introduce a novel generalized model for generating recommendations over a search context. While we only consider query text in this study, the model can handle integration over arbitrary user search behavior, such as page visits, dwell times, and query abandonment. In addition, it can be used for other types of recommendation, including personalized web search.
{"title":"Task-aware query recommendation","authors":"H. Feild, James Allan","doi":"10.1145/2484028.2484069","DOIUrl":"https://doi.org/10.1145/2484028.2484069","url":null,"abstract":"When generating query recommendations for a user, a natural approach is to try and leverage not only the user's most recently submitted query, or reference query, but also information about the current search context, such as the user's recent search interactions. We focus on two important classes of queries that make up search contexts: those that address the same information need as the reference query (on-task queries), and those that do not (off-task queries). We analyze the effects on query recommendation performance of using contexts consisting of only on-task queries, only off-task queries, and a mix of the two. Using TREC Session Track data for simulations, we demonstrate that on-task context is helpful on average but can be easily overwhelmed when off-task queries are interleaved---a common situation according to several analyses of commercial search logs. To minimize the impact of off-task queries on recommendation performance, we consider automatic methods of identifying such queries using a state of the art search task identification technique. Our experimental results show that automatic search task identification can eliminate the effect of off-task queries in a mixed context. We also introduce a novel generalized model for generating recommendations over a search context. While we only consider query text in this study, the model can handle integration over arbitrary user search behavior, such as page visits, dwell times, and query abandonment. In addition, it can be used for other types of recommendation, including personalized web search.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122054396","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}
Distributed events are collections of events taking place within a small area over the same time period and relating to a single topic. There are often a large number of events on offer and the times in which they can be visited are heavily constrained, therefore the task of choosing events to visit and in which order can be very difficult. In this work we investigate how visitors can be assisted by means of a recommender system via 2 large-scale naturalistic studies (n=860 and n=1047). We show that a recommender system can influence users to select events that result in tighter and more compact routes, thus allowing users to spend less time travelling and more time visiting events.
{"title":"RecSys for distributed events: investigating the influence of recommendations on visitor plans","authors":"Richard Schaller, Morgan Harvey, David Elsweiler","doi":"10.1145/2484028.2484119","DOIUrl":"https://doi.org/10.1145/2484028.2484119","url":null,"abstract":"Distributed events are collections of events taking place within a small area over the same time period and relating to a single topic. There are often a large number of events on offer and the times in which they can be visited are heavily constrained, therefore the task of choosing events to visit and in which order can be very difficult. In this work we investigate how visitors can be assisted by means of a recommender system via 2 large-scale naturalistic studies (n=860 and n=1047). We show that a recommender system can influence users to select events that result in tighter and more compact routes, thus allowing users to spend less time travelling and more time visiting events.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"XCV 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131394161","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}
The task of finding groups is a natural extension of search tasks aimed at retrieving individual entities. We introduce a group finding task: given a query topic, find knowledgeable groups that have expertise on that topic. We present four general strategies to this task. The models are formalized using generative language models. Two of the models aggregate expertise scores of the experts in the same group for the task, one locates documents associated with experts in the group and then determines how closely the documents are associated with the topic, whilst the remaining model directly estimates the degree to which a group is a knowledgeable group for a given topic. We construct a test collections based on the TREC 2005 and 2006 Enterprise collections. We find significant differences between different ways of estimating the association between a topic and a group. Experiments show that our knowledgeable group finding models achieve high absolute scores.
{"title":"Finding knowledgeable groups in enterprise corpora","authors":"Shangsong Liang, M. de Rijke","doi":"10.1145/2484028.2484109","DOIUrl":"https://doi.org/10.1145/2484028.2484109","url":null,"abstract":"The task of finding groups is a natural extension of search tasks aimed at retrieving individual entities. We introduce a group finding task: given a query topic, find knowledgeable groups that have expertise on that topic. We present four general strategies to this task. The models are formalized using generative language models. Two of the models aggregate expertise scores of the experts in the same group for the task, one locates documents associated with experts in the group and then determines how closely the documents are associated with the topic, whilst the remaining model directly estimates the degree to which a group is a knowledgeable group for a given topic. We construct a test collections based on the TREC 2005 and 2006 Enterprise collections. We find significant differences between different ways of estimating the association between a topic and a group. Experiments show that our knowledgeable group finding models achieve high absolute scores.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131930042","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}
Mobile devices provide people with a conduit to the rich infor-mation resources of the Web. With consent, the devices can also provide streams of information about search activity and location that can be used in population studies and real-time assistance. We analyzed geotagged mobile queries in a privacy-sensitive study of potential transitions from health information search to in-world healthcare utilization. We note differences in people's health infor-mation seeking before, during, and after the appearance of evidence that a medical facility has been visited. We find that we can accu-rately estimate statistics about such potential user engagement with healthcare providers. The findings highlight the promise of using geocoded search for sensing and predicting activities in the world.
{"title":"Pursuing insights about healthcare utilization via geocoded search queries","authors":"Shuang-Hong Yang, Ryen W. White, E. Horvitz","doi":"10.1145/2484028.2484147","DOIUrl":"https://doi.org/10.1145/2484028.2484147","url":null,"abstract":"Mobile devices provide people with a conduit to the rich infor-mation resources of the Web. With consent, the devices can also provide streams of information about search activity and location that can be used in population studies and real-time assistance. We analyzed geotagged mobile queries in a privacy-sensitive study of potential transitions from health information search to in-world healthcare utilization. We note differences in people's health infor-mation seeking before, during, and after the appearance of evidence that a medical facility has been visited. We find that we can accu-rately estimate statistics about such potential user engagement with healthcare providers. The findings highlight the promise of using geocoded search for sensing and predicting activities in the world.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133181233","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}
Ricardo Ribeiro, Luís Marujo, David Martins de Matos, J. Neto, A. Gershman, J. Carbonell
In general, centrality-based retrieval models treat all elements of the retrieval space equally, which may reduce their effectiveness. In the specific context of extractive summarization (or important passage retrieval), this means that these models do not take into account that information sources often contain lateral issues, which are hardly as important as the description of the main topic, or are composed by mixtures of topics. We present a new two-stage method that starts by extracting a collection of key phrases that will be used to help centrality-as-relevance retrieval model. We explore several approaches to the integration of the key phrases in the centrality model. The proposed method is evaluated using different datasets that vary in noise (noisy vs clean) and language (Portuguese vs English). Results show that the best variant achieves relative performance improvements of about 31% in clean data and 18% in noisy data.
{"title":"Self reinforcement for important passage retrieval","authors":"Ricardo Ribeiro, Luís Marujo, David Martins de Matos, J. Neto, A. Gershman, J. Carbonell","doi":"10.1145/2484028.2484134","DOIUrl":"https://doi.org/10.1145/2484028.2484134","url":null,"abstract":"In general, centrality-based retrieval models treat all elements of the retrieval space equally, which may reduce their effectiveness. In the specific context of extractive summarization (or important passage retrieval), this means that these models do not take into account that information sources often contain lateral issues, which are hardly as important as the description of the main topic, or are composed by mixtures of topics. We present a new two-stage method that starts by extracting a collection of key phrases that will be used to help centrality-as-relevance retrieval model. We explore several approaches to the integration of the key phrases in the centrality model. The proposed method is evaluated using different datasets that vary in noise (noisy vs clean) and language (Portuguese vs English). Results show that the best variant achieves relative performance improvements of about 31% in clean data and 18% in noisy data.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133722239","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}
Zhuo Li, Matthew Stagitis, S. Carberry, Kathleen F. McCoy
Information retrieval research has made significant progress in the retrieval of text documents and images. However, relatively little attention has been given to the retrieval of information graphics (non-pictorial images such as bar charts and line graphs) despite their proliferation in popular media such as newspapers and magazines. Our goal is to build a system for retrieving bar charts and line graphs that reasons about the content of the graphic itself in deciding its relevance to the user query. This paper presents the first steps toward such a system, with a focus on identifying the category of intended message of potentially relevant bar charts and line graphs. Our learned model achieves accuracy higher than 80% on a corpus of collected user queries.
{"title":"Towards retrieving relevant information graphics","authors":"Zhuo Li, Matthew Stagitis, S. Carberry, Kathleen F. McCoy","doi":"10.1145/2484028.2484164","DOIUrl":"https://doi.org/10.1145/2484028.2484164","url":null,"abstract":"Information retrieval research has made significant progress in the retrieval of text documents and images. However, relatively little attention has been given to the retrieval of information graphics (non-pictorial images such as bar charts and line graphs) despite their proliferation in popular media such as newspapers and magazines. Our goal is to build a system for retrieving bar charts and line graphs that reasons about the content of the graphic itself in deciding its relevance to the user query. This paper presents the first steps toward such a system, with a focus on identifying the category of intended message of potentially relevant bar charts and line graphs. Our learned model achieves accuracy higher than 80% on a corpus of collected user queries.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"362 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133399570","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}
The reliability of a test collection is proportional to the number of queries it contains. But building a collection with many queries is expensive, so researchers have to find a balance between reliability and cost. Previous work on the measurement of test collection reliability relied on data-based approaches that contemplated random what if scenarios, and provided indicators such as swap rates and Kendall tau correlations. Generalizability Theory was proposed as an alternative founded on analysis of variance that provides reliability indicators based on statistical theory. However, these reliability indicators are hard to interpret in practice, because they do not correspond to well known indicators like Kendall tau correlation. We empirically established these relationships based on data from over 40 TREC collections, thus filling the gap in the practical interpretation of Generalizability Theory. We also review the computation of these indicators, and show that they are extremely dependent on the sample of systems and queries used, so much that the required number of queries to achieve a certain level of reliability can vary in orders of magnitude. We discuss the computation of confidence intervals for these statistics, providing a much more reliable tool to measure test collection reliability. Reflecting upon all these results, we review a wealth of TREC test collections, arguing that they are possibly not as reliable as generally accepted and that the common choice of 50 queries is insufficient even for stable rankings.
{"title":"On the measurement of test collection reliability","authors":"Julián Urbano, M. Marrero, Diego Martín","doi":"10.1145/2484028.2484038","DOIUrl":"https://doi.org/10.1145/2484028.2484038","url":null,"abstract":"The reliability of a test collection is proportional to the number of queries it contains. But building a collection with many queries is expensive, so researchers have to find a balance between reliability and cost. Previous work on the measurement of test collection reliability relied on data-based approaches that contemplated random what if scenarios, and provided indicators such as swap rates and Kendall tau correlations. Generalizability Theory was proposed as an alternative founded on analysis of variance that provides reliability indicators based on statistical theory. However, these reliability indicators are hard to interpret in practice, because they do not correspond to well known indicators like Kendall tau correlation. We empirically established these relationships based on data from over 40 TREC collections, thus filling the gap in the practical interpretation of Generalizability Theory. We also review the computation of these indicators, and show that they are extremely dependent on the sample of systems and queries used, so much that the required number of queries to achieve a certain level of reliability can vary in orders of magnitude. We discuss the computation of confidence intervals for these statistics, providing a much more reliable tool to measure test collection reliability. Reflecting upon all these results, we review a wealth of TREC test collections, arguing that they are possibly not as reliable as generally accepted and that the common choice of 50 queries is insufficient even for stable rankings.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114935555","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}
Makoto P. Kato, T. Sakai, Takehiro Yamamoto, Mayu Iwata
The One Click Access Task (1CLICK) of NTCIR requires systems to return a concise multi-document summary of web pages in response to a query which is assumed to have been submitted in a mobile context. Systems are evaluated based on information units (or iUnits), and are required to present important pieces of information first and to minimise the amount of text the user has to read. Using the official Japanese results of the second round of the 1CLICK task from NTCIR-10, we discuss our task setting and evaluation framework. Our analyses show that: (1) Simple baseline methods that leverage search engine snippets or Wikipedia are effective for 'lookup' type queries but not necessarily for other query types; (2) There is still a substantial gap between manual and automatic runs; and (3) Our evaluation metrics are relatively robust to the incompleteness of iUnits.
{"title":"Report from the NTCIR-10 1CLICK-2 Japanese subtask: baselines, upperbounds and evaluation robustness","authors":"Makoto P. Kato, T. Sakai, Takehiro Yamamoto, Mayu Iwata","doi":"10.1145/2484028.2484117","DOIUrl":"https://doi.org/10.1145/2484028.2484117","url":null,"abstract":"The One Click Access Task (1CLICK) of NTCIR requires systems to return a concise multi-document summary of web pages in response to a query which is assumed to have been submitted in a mobile context. Systems are evaluated based on information units (or iUnits), and are required to present important pieces of information first and to minimise the amount of text the user has to read. Using the official Japanese results of the second round of the 1CLICK task from NTCIR-10, we discuss our task setting and evaluation framework. Our analyses show that: (1) Simple baseline methods that leverage search engine snippets or Wikipedia are effective for 'lookup' type queries but not necessarily for other query types; (2) There is still a substantial gap between manual and automatic runs; and (3) Our evaluation metrics are relatively robust to the incompleteness of iUnits.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"512 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116700661","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}
Margarita Karkali, Dimitris Pontikis, M. Vazirgiannis
We present Match the News, a browser extension for real time news recommendation. Our extension works on the client side to recommend in real time recently published articles that are relevant to the web page the user is currently visiting. Match the News is fed from Google News RSS and applies syntactic matching to find the relevant articles. We implement an innovative weighting function to perform the keyword extraction task, BM25H. With BM25H we extract keywords not only relevant to currently browsed web page, but also novel with respect to the user's recent browsing history. The novelty feature in keyword extraction task results in meaningful news recommendations with regards to the web page the users currently visits. Moreover the extension offers a salient visualization of the terms corresponding to the users recent browsing history making thus the extension a comprehensive tool for real time news recommendation and self assessment.
我们现在匹配的新闻，实时新闻推荐的浏览器扩展。我们的扩展工作在客户端实时推荐最近发表的文章是相关的网页用户目前正在访问。Match the News从Google News RSS提供，并应用语法匹配来查找相关文章。我们实现了一个创新的加权函数来执行关键字提取任务，BM25H。使用BM25H，我们提取的关键字不仅与当前浏览的网页相关，而且与用户最近的浏览历史有关。关键字提取任务中的新颖性功能会对用户当前访问的网页产生有意义的新闻推荐。此外，扩展提供了与用户最近浏览历史相对应的显著可视化术语，从而使扩展成为实时新闻推荐和自我评估的综合工具。
{"title":"Match the news: a firefox extension for real-time news recommendation","authors":"Margarita Karkali, Dimitris Pontikis, M. Vazirgiannis","doi":"10.1145/2484028.2484208","DOIUrl":"https://doi.org/10.1145/2484028.2484208","url":null,"abstract":"We present Match the News, a browser extension for real time news recommendation. Our extension works on the client side to recommend in real time recently published articles that are relevant to the web page the user is currently visiting. Match the News is fed from Google News RSS and applies syntactic matching to find the relevant articles. We implement an innovative weighting function to perform the keyword extraction task, BM25H. With BM25H we extract keywords not only relevant to currently browsed web page, but also novel with respect to the user's recent browsing history. The novelty feature in keyword extraction task results in meaningful news recommendations with regards to the web page the users currently visits. Moreover the extension offers a salient visualization of the terms corresponding to the users recent browsing history making thus the extension a comprehensive tool for real time news recommendation and self assessment.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116707759","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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