The tutorial is based on our long-term research on open domain conversation, rich hands-on experience on development of Microsoft XiaoIce, and our previous tutorials on EMNLP 2018 and the Web Conference 2019. It starts from a summary of recent achievement made by both academia and industry on chatbots, and then performs a thorough and systematic introduction to state-of-the-art methods for open domain conversation modeling including both retrieval-based methods and generation-based methods. In addition to these, the tutorial also covers some new progress on both groups of methods, such as transition from model design to model learning, transition from knowledge agnostic conversation to knowledge aware conversation, and transition from single-modal conversation to multi-modal conversation. The tutorial is ended by some promising future directions such as how to combine non-task-oriented dialogue systems with task-oriented dialogue systems and how to enhance language learning with chatbots.
{"title":"Deep Chit-Chat: Deep Learning for Chatbots","authors":"Wei Wu, Rui Yan","doi":"10.1145/3331184.3331388","DOIUrl":"https://doi.org/10.1145/3331184.3331388","url":null,"abstract":"The tutorial is based on our long-term research on open domain conversation, rich hands-on experience on development of Microsoft XiaoIce, and our previous tutorials on EMNLP 2018 and the Web Conference 2019. It starts from a summary of recent achievement made by both academia and industry on chatbots, and then performs a thorough and systematic introduction to state-of-the-art methods for open domain conversation modeling including both retrieval-based methods and generation-based methods. In addition to these, the tutorial also covers some new progress on both groups of methods, such as transition from model design to model learning, transition from knowledge agnostic conversation to knowledge aware conversation, and transition from single-modal conversation to multi-modal conversation. The tutorial is ended by some promising future directions such as how to combine non-task-oriented dialogue systems with task-oriented dialogue systems and how to enhance language learning with chatbots.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"232 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72896521","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}
We revisit the Normalized Query Commitment (NQC) query performance prediction (QPP) method. To this end, we suggest a scaled extension to a discriminative QPP framework and use it to analyze NQC. Using this analysis allows us to redesign NQC and suggest several options for improvement.
{"title":"Normalized Query Commitment Revisited","authors":"Haggai Roitman","doi":"10.1145/3331184.3331334","DOIUrl":"https://doi.org/10.1145/3331184.3331334","url":null,"abstract":"We revisit the Normalized Query Commitment (NQC) query performance prediction (QPP) method. To this end, we suggest a scaled extension to a discriminative QPP framework and use it to analyze NQC. Using this analysis allows us to redesign NQC and suggest several options for improvement.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74084262","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":"Session details: Session 6A: Social Media","authors":"J. Mothe","doi":"10.1145/3349689","DOIUrl":"https://doi.org/10.1145/3349689","url":null,"abstract":"","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"221 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72578140","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}
Sound effects play an essential role in producing high-quality radio stories but require enormous labor cost to add. In this paper, we address the problem of automatically adding sound effects to radio stories with a retrieval-based model. However, directly implementing a tag-based retrieval model leads to high false positives due to the ambiguity of story contents. To solve this problem, we introduce a retrieval-based framework hybridized with a semantic inference model which helps to achieve robust retrieval results. Our model relies on fine-designed features extracted from the context of candidate triggers. We collect two story dubbing datasets through crowdsourcing to analyze the setting of adding sound effects and to train and test our proposed methods. We further discuss the importance of each feature and introduce several heuristic rules for the trade-off between precision and recall. Together with the text-to-speech technology, our results reveal a promising automatic pipeline on producing high-quality radio stories.
{"title":"From Text to Sound: A Preliminary Study on Retrieving Sound Effects to Radio Stories","authors":"Songwei Ge, Curtis Xuan, Ruihua Song, Chao Zou, Wei Liu, Jin Zhou","doi":"10.1145/3331184.3331274","DOIUrl":"https://doi.org/10.1145/3331184.3331274","url":null,"abstract":"Sound effects play an essential role in producing high-quality radio stories but require enormous labor cost to add. In this paper, we address the problem of automatically adding sound effects to radio stories with a retrieval-based model. However, directly implementing a tag-based retrieval model leads to high false positives due to the ambiguity of story contents. To solve this problem, we introduce a retrieval-based framework hybridized with a semantic inference model which helps to achieve robust retrieval results. Our model relies on fine-designed features extracted from the context of candidate triggers. We collect two story dubbing datasets through crowdsourcing to analyze the setting of adding sound effects and to train and test our proposed methods. We further discuss the importance of each feature and introduce several heuristic rules for the trade-off between precision and recall. Together with the text-to-speech technology, our results reveal a promising automatic pipeline on producing high-quality radio stories.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74648903","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}
Xin Zhou, Yating Zhang, Xiaozhong Liu, Changlong Sun, Luo Si
Various e-commerce platforms produce millions of transactions per day with many transaction disputes. This generates the demand for effective and efficient dispute resolutions for e-commerce transactions. This paper proposes a novel research task of Legal Dispute Judgment (LDJ) prediction for e-commerce transactions, which connects two yet isolated domains, e-commerce data mining and legal intelligence. Different from traditional legal intelligence with the focus on textual evidence of the dispute itself, the new research utilizes multiview information such as past behavior information of seller and buyer as well as textual evidence of the current transaction. The multiview dispute representation is integrated into an innovative multi-task learning framework for predicting the legal result. An extensive set of experiments with a large dispute case dataset collected from a world leading e-commerce platform shows that the proposed model can more accurately characterize a dispute case through buyer, seller, and transaction viewpoints for legal judgment prediction against several alternatives.
{"title":"Legal Intelligence for E-commerce: Multi-task Learning by Leveraging Multiview Dispute Representation","authors":"Xin Zhou, Yating Zhang, Xiaozhong Liu, Changlong Sun, Luo Si","doi":"10.1145/3331184.3331212","DOIUrl":"https://doi.org/10.1145/3331184.3331212","url":null,"abstract":"Various e-commerce platforms produce millions of transactions per day with many transaction disputes. This generates the demand for effective and efficient dispute resolutions for e-commerce transactions. This paper proposes a novel research task of Legal Dispute Judgment (LDJ) prediction for e-commerce transactions, which connects two yet isolated domains, e-commerce data mining and legal intelligence. Different from traditional legal intelligence with the focus on textual evidence of the dispute itself, the new research utilizes multiview information such as past behavior information of seller and buyer as well as textual evidence of the current transaction. The multiview dispute representation is integrated into an innovative multi-task learning framework for predicting the legal result. An extensive set of experiments with a large dispute case dataset collected from a world leading e-commerce platform shows that the proposed model can more accurately characterize a dispute case through buyer, seller, and transaction viewpoints for legal judgment prediction against several alternatives.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75007716","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}
Diksha Garg, Priyanka Gupta, Pankaj Malhotra, L. Vig, Gautam M. Shroff
Recent advances in sequence-aware approaches for session-based recommendation, such as those based on recurrent neural networks, highlight the importance of leveraging sequential information from a session while making recommendations. Further, a session based k-nearest-neighbors approach (SKNN) has proven to be a strong baseline for session-based recommendations. However, SKNN does not take into account the readily available sequential and temporal information from sessions. In this work, we propose Sequence and Time Aware Neighborhood (STAN), with vanilla SKNN as its special case. STAN takes into account the following factors for making recommendations: i) position of an item in the current session, ii) recency of a past session w.r.t. to the current session, and iii) position of a recommendable item in a neighboring session. The importance of above factors for a specific application can be adjusted via controllable decay factors. Despite being simple, intuitive and easy to implement, empirical evaluation on three real-world datasets shows that STAN significantly improves over SKNN, and is even comparable to the recently proposed state-of-the-art deep learning approaches. Our results suggest that STAN can be considered as a strong baseline for evaluating session-based recommendation algorithms in future.
{"title":"Sequence and Time Aware Neighborhood for Session-based Recommendations: STAN","authors":"Diksha Garg, Priyanka Gupta, Pankaj Malhotra, L. Vig, Gautam M. Shroff","doi":"10.1145/3331184.3331322","DOIUrl":"https://doi.org/10.1145/3331184.3331322","url":null,"abstract":"Recent advances in sequence-aware approaches for session-based recommendation, such as those based on recurrent neural networks, highlight the importance of leveraging sequential information from a session while making recommendations. Further, a session based k-nearest-neighbors approach (SKNN) has proven to be a strong baseline for session-based recommendations. However, SKNN does not take into account the readily available sequential and temporal information from sessions. In this work, we propose Sequence and Time Aware Neighborhood (STAN), with vanilla SKNN as its special case. STAN takes into account the following factors for making recommendations: i) position of an item in the current session, ii) recency of a past session w.r.t. to the current session, and iii) position of a recommendable item in a neighboring session. The importance of above factors for a specific application can be adjusted via controllable decay factors. Despite being simple, intuitive and easy to implement, empirical evaluation on three real-world datasets shows that STAN significantly improves over SKNN, and is even comparable to the recently proposed state-of-the-art deep learning approaches. Our results suggest that STAN can be considered as a strong baseline for evaluating session-based recommendation algorithms in future.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72644553","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}
At Ancestry, we apply learning to rank algorithms to a new area to assist our customers in better understanding their family history. The foundation of our service is an extensive and unique collection of billions of historical records that we have digitized and indexed. Currently, our content collection includes 20 billion historical records. The record data consists of birth records, death records, marriage records, adoption records, census records, obituary records, among many others types. It is important for us to return relevant records from diversified record types in order to assist our customers to better understand their family history.
{"title":"Family History Discovery through Search at Ancestry","authors":"Peng Jiang, Yingrui Yang, Gann Bierner, F. Li, Ruhan Wang, Azadeh Moghtaderi","doi":"10.1145/3331184.3331430","DOIUrl":"https://doi.org/10.1145/3331184.3331430","url":null,"abstract":"At Ancestry, we apply learning to rank algorithms to a new area to assist our customers in better understanding their family history. The foundation of our service is an extensive and unique collection of billions of historical records that we have digitized and indexed. Currently, our content collection includes 20 billion historical records. The record data consists of birth records, death records, marriage records, adoption records, census records, obituary records, among many others types. It is important for us to return relevant records from diversified record types in order to assist our customers to better understand their family history.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75070138","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}
Social media users create millions of microblog entries on various topics each day. Retweet behaviour play a crucial role in spreading topics on social media. Retweet prediction task has received considerable attention in recent years. The majority of existing retweet prediction methods are focus on modeling user preference by utilizing various information, such as user profiles, user post history, user following relationships, etc. Yet, the users exposures towards real-time posting from their followees contribute significantly to making retweet predictions, considering that the users may participate into the hot topics discussed by their followees rather than be limited to their previous interests. To make efficient use of hot topics, we propose a novel masked self-attentive model to perform the retweet prediction task by perceiving the hot topics discussed by the users' followees. We incorporate the posting histories of users with external memory and utilize a hierarchical attention mechanism to construct the users' interests. Hence, our model can be jointly hot-topic aware and user interests aware to make a final prediction. Experimental results on a dataset collected from Twitter demonstrated that the proposed method can achieve better performance than state-of-the-art methods.
{"title":"Hot Topic-Aware Retweet Prediction with Masked Self-attentive Model","authors":"Renfeng Ma, Xiangkun Hu, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang","doi":"10.1145/3331184.3331236","DOIUrl":"https://doi.org/10.1145/3331184.3331236","url":null,"abstract":"Social media users create millions of microblog entries on various topics each day. Retweet behaviour play a crucial role in spreading topics on social media. Retweet prediction task has received considerable attention in recent years. The majority of existing retweet prediction methods are focus on modeling user preference by utilizing various information, such as user profiles, user post history, user following relationships, etc. Yet, the users exposures towards real-time posting from their followees contribute significantly to making retweet predictions, considering that the users may participate into the hot topics discussed by their followees rather than be limited to their previous interests. To make efficient use of hot topics, we propose a novel masked self-attentive model to perform the retweet prediction task by perceiving the hot topics discussed by the users' followees. We incorporate the posting histories of users with external memory and utilize a hierarchical attention mechanism to construct the users' interests. Hence, our model can be jointly hot-topic aware and user interests aware to make a final prediction. Experimental results on a dataset collected from Twitter demonstrated that the proposed method can achieve better performance than state-of-the-art methods.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76013320","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}
Yuqi Huo, Yao Lu, Yulei Niu, Zhiwu Lu, Ji-Rong Wen
Fine-grained image classification and retrieval become topical in both computer vision and information retrieval. In real-life scenarios, fine-grained tasks tend to appear along with coarse-grained tasks when the observed object is coming closer. However, in previous works, the combination of fine-grained and coarse-grained tasks was often ignored. In this paper, we define a new problem called coarse-to-fine grained classification (C2FGC) which aims to recognize the classes of objects in multiple resolutions (from low to high). To solve this problem, we propose a novel Multi-linear Pooling with Hierarchy (MLPH) model. Specifically, we first design a multi-linear pooling module to include both trilinear and bilinear pooling, and then formulate the coarse-grained and fine-grained tasks within a unified framework. Experiments on two benchmark datasets show that our model achieves state-of-the-art results.
{"title":"Coarse-to-Fine Grained Classification","authors":"Yuqi Huo, Yao Lu, Yulei Niu, Zhiwu Lu, Ji-Rong Wen","doi":"10.1145/3331184.3331336","DOIUrl":"https://doi.org/10.1145/3331184.3331336","url":null,"abstract":"Fine-grained image classification and retrieval become topical in both computer vision and information retrieval. In real-life scenarios, fine-grained tasks tend to appear along with coarse-grained tasks when the observed object is coming closer. However, in previous works, the combination of fine-grained and coarse-grained tasks was often ignored. In this paper, we define a new problem called coarse-to-fine grained classification (C2FGC) which aims to recognize the classes of objects in multiple resolutions (from low to high). To solve this problem, we propose a novel Multi-linear Pooling with Hierarchy (MLPH) model. Specifically, we first design a multi-linear pooling module to include both trilinear and bilinear pooling, and then formulate the coarse-grained and fine-grained tasks within a unified framework. Experiments on two benchmark datasets show that our model achieves state-of-the-art results.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82564302","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}
M. Albakour, Miguel Martinez, S. Tippmann, Ahmet Aker, J. Stray, Shiri Dori-Hacohen, Alberto Barrón-Cedeño
The journalism industry has undergone a revolution in the past decade, leading to new opportunities as well as challenges. News consumption, production and delivery have all been affected and transformed by technology Readers require new mechanisms to cope with the vast volume of information in order to be informed about news events. Reporters have begun to use natural language processing (NLP) and (IR) techniques for investigative work. Publishers and aggregators are seeking new business models, and new ways to reach and retain their audience. A shift in business models has led to a gradual shift in styles of journalism in attempts to increase page views; and, far more concerning, to real mis- and dis-information, alongside allegations of "fake news" threatening the journalistic freedom and integrity of legitimate news outlets. Social media platforms drive viewership, creating filter bubbles and an increasingly polarized readership. News documents have always been a part of research on information access and retrieval methods. Over the last few years, the IR community has increasingly recognized these challenges in journalism and opened a conversation about how we might begin to address them. Evidence of this recognition is the participation in the two previous editions of our NewsIR workshop, held in ECIR 2016 and 2018. One of the most important outcomes of those workshops is an increasing awareness in the community about the changing nature of journalism and the IR challenges it entails. To move yet another step forward, the goal of the third edition of our workshop will be to create a multidisciplinary venue that brings together news experts from both technology and journalism. This would take NewsIR from a European forum targeting mainly IR researchers, into a more inclusive and influential international forum. We hope that this new format will foster further understanding for both news professionals and IR researchers, as well as producing better outcomes for news consumers. We will address the possibilities and challenges that technology offers to the journalists, the challenges that new developments in journalism create for IR researchers, and the complexity of information access tasks for news readers.
{"title":"Third International Workshop on Recent Trends in News Information Retrieval (NewsIR'19)","authors":"M. Albakour, Miguel Martinez, S. Tippmann, Ahmet Aker, J. Stray, Shiri Dori-Hacohen, Alberto Barrón-Cedeño","doi":"10.1145/3331184.3331646","DOIUrl":"https://doi.org/10.1145/3331184.3331646","url":null,"abstract":"The journalism industry has undergone a revolution in the past decade, leading to new opportunities as well as challenges. News consumption, production and delivery have all been affected and transformed by technology Readers require new mechanisms to cope with the vast volume of information in order to be informed about news events. Reporters have begun to use natural language processing (NLP) and (IR) techniques for investigative work. Publishers and aggregators are seeking new business models, and new ways to reach and retain their audience. A shift in business models has led to a gradual shift in styles of journalism in attempts to increase page views; and, far more concerning, to real mis- and dis-information, alongside allegations of \"fake news\" threatening the journalistic freedom and integrity of legitimate news outlets. Social media platforms drive viewership, creating filter bubbles and an increasingly polarized readership. News documents have always been a part of research on information access and retrieval methods. Over the last few years, the IR community has increasingly recognized these challenges in journalism and opened a conversation about how we might begin to address them. Evidence of this recognition is the participation in the two previous editions of our NewsIR workshop, held in ECIR 2016 and 2018. One of the most important outcomes of those workshops is an increasing awareness in the community about the changing nature of journalism and the IR challenges it entails. To move yet another step forward, the goal of the third edition of our workshop will be to create a multidisciplinary venue that brings together news experts from both technology and journalism. This would take NewsIR from a European forum targeting mainly IR researchers, into a more inclusive and influential international forum. We hope that this new format will foster further understanding for both news professionals and IR researchers, as well as producing better outcomes for news consumers. We will address the possibilities and challenges that technology offers to the journalists, the challenges that new developments in journalism create for IR researchers, and the complexity of information access tasks for news readers.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89517465","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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