Yu-Chin Juan, Yong Zhuang, Wei-Sheng Chin, Chih-Jen Lin
Click-through rate (CTR) prediction plays an important role in computational advertising. Models based on degree-2 polynomial mappings and factorization machines (FMs) are widely used for this task. Recently, a variant of FMs, field-aware factorization machines (FFMs), outperforms existing models in some world-wide CTR-prediction competitions. Based on our experiences in winning two of them, in this paper we establish FFMs as an effective method for classifying large sparse data including those from CTR prediction. First, we propose efficient implementations for training FFMs. Then we comprehensively analyze FFMs and compare this approach with competing models. Experiments show that FFMs are very useful for certain classification problems. Finally, we have released a package of FFMs for public use.
{"title":"Field-aware Factorization Machines for CTR Prediction","authors":"Yu-Chin Juan, Yong Zhuang, Wei-Sheng Chin, Chih-Jen Lin","doi":"10.1145/2959100.2959134","DOIUrl":"https://doi.org/10.1145/2959100.2959134","url":null,"abstract":"Click-through rate (CTR) prediction plays an important role in computational advertising. Models based on degree-2 polynomial mappings and factorization machines (FMs) are widely used for this task. Recently, a variant of FMs, field-aware factorization machines (FFMs), outperforms existing models in some world-wide CTR-prediction competitions. Based on our experiences in winning two of them, in this paper we establish FFMs as an effective method for classifying large sparse data including those from CTR prediction. First, we propose efficient implementations for training FFMs. Then we comprehensively analyze FFMs and compare this approach with competing models. Experiments show that FFMs are very useful for certain classification problems. Finally, we have released a package of FFMs for public use.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123419920","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}
Foursquare recently launched Marsbot, an SMS-based app for local recommendations. Marsbot is an intelligent friend that lives in your pocket and learns about you through the places you go in the real world. While this product is aligned with Foursquare's long-standing mission to find the best places, it represents a new era in the way people interact with recommendation engines. The promise of the latest crop of personal assistants is get us information more quickly and seamlessly, but building them comes with many challenges. In this talk, we discuss why we built Marsbot and some of the many lessons learned along the way.
{"title":"Marsbot: Building a Personal Assistant","authors":"Max Sklar","doi":"10.1145/2959100.2959119","DOIUrl":"https://doi.org/10.1145/2959100.2959119","url":null,"abstract":"Foursquare recently launched Marsbot, an SMS-based app for local recommendations. Marsbot is an intelligent friend that lives in your pocket and learns about you through the places you go in the real world. While this product is aligned with Foursquare's long-standing mission to find the best places, it represents a new era in the way people interact with recommendation engines. The promise of the latest crop of personal assistants is get us information more quickly and seamlessly, but building them comes with many challenges. In this talk, we discuss why we built Marsbot and some of the many lessons learned along the way.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124629312","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}
Roberto Pagano, P. Cremonesi, M. Larson, Balázs Hidasi, D. Tikk, Alexandros Karatzoglou, Massimo Quadrana
A critical change has occurred in the status of context in recommender systems. In the past, context has been considered 'additional evidence'. This past picture is at odds with many present application domains, where user and item information is scarce. Such domains face continuous cold start conditions and must exploit session rather than user information. In this paper, we describe the `Contextual Turn?: the move towards context-driven recommendation algorithms for which context is critical, rather than additional. We cover application domains, algorithms that promise to address the challenges of context-driven recommendation, and the steps that the community has taken to tackle context-driven problems. Our goal is to point out the commonalities of context-driven problems, and urge the community to address the overarching challenges that context-driven recommendation poses.
{"title":"The Contextual Turn: from Context-Aware to Context-Driven Recommender Systems","authors":"Roberto Pagano, P. Cremonesi, M. Larson, Balázs Hidasi, D. Tikk, Alexandros Karatzoglou, Massimo Quadrana","doi":"10.1145/2959100.2959136","DOIUrl":"https://doi.org/10.1145/2959100.2959136","url":null,"abstract":"A critical change has occurred in the status of context in recommender systems. In the past, context has been considered 'additional evidence'. This past picture is at odds with many present application domains, where user and item information is scarce. Such domains face continuous cold start conditions and must exploit session rather than user information. In this paper, we describe the `Contextual Turn?: the move towards context-driven recommendation algorithms for which context is critical, rather than additional. We cover application domains, algorithms that promise to address the challenges of context-driven recommendation, and the steps that the community has taken to tackle context-driven problems. Our goal is to point out the commonalities of context-driven problems, and urge the community to address the overarching challenges that context-driven recommendation poses.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121641530","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}
Query recommendation predominantly relies on search logs to use existing queries for recommendation, typically calculating query similarity metrics or transition probabilities from the log. While effective, such recommendations are limited to the queries, words, and phrases in the log. They hence do not recommend potentially useful, entirely novel queries. Recent query recommendation methods have proposed generating queries on a topical or thematic level, though current approaches are limited to generating single words. We propose a hybrid method for constructing multi-word queries in this generative sense. It uses Latent Dirichlet Allocation to generate a topic for exploration and skip-gram modeling to generate queries from the topic. According to additional evaluation metrics we present, our model improves diversity and has some room for improving relevance, yet offers an interesting avenue for query recommendation.
{"title":"Multi-Word Generative Query Recommendation Using Topic Modeling","authors":"M. Mitsui, C. Shah","doi":"10.1145/2959100.2959154","DOIUrl":"https://doi.org/10.1145/2959100.2959154","url":null,"abstract":"Query recommendation predominantly relies on search logs to use existing queries for recommendation, typically calculating query similarity metrics or transition probabilities from the log. While effective, such recommendations are limited to the queries, words, and phrases in the log. They hence do not recommend potentially useful, entirely novel queries. Recent query recommendation methods have proposed generating queries on a topical or thematic level, though current approaches are limited to generating single words. We propose a hybrid method for constructing multi-word queries in this generative sense. It uses Latent Dirichlet Allocation to generate a topic for exploration and skip-gram modeling to generate queries from the topic. According to additional evaluation metrics we present, our model improves diversity and has some room for improving relevance, yet offers an interesting avenue for query recommendation.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134155124","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}
Short-length random walks on the bipartite user-item graph have recently been shown to provide accurate and diverse recommendations. Nonetheless, these approaches suffer from severe time and space requirements, which can be alleviated via random walk sampling, at the cost of reduced recommendation quality. In addition, these approaches ignore users' ratings, which further limits their expressiveness. In this paper, we introduce a computationally efficient graph-based approach for collaborative filtering based on short-path enumeration. Moreover, we propose three scoring functions based on the Bayesian paradigm that effectively exploit distributional aspects of the users' ratings. We experiment with seven publicly available datasets against state-of-the-art graph-based and matrix factorization approaches. Our empirical results demonstrate the effectiveness of the proposed approach, with significant improvements in most settings. Furthermore, analytical results demonstrate its efficiency compared to other graph-based approaches.
{"title":"Efficient Bayesian Methods for Graph-based Recommendation","authors":"Ramon Lopes, R. Assunção, Rodrygo L. T. Santos","doi":"10.1145/2959100.2959132","DOIUrl":"https://doi.org/10.1145/2959100.2959132","url":null,"abstract":"Short-length random walks on the bipartite user-item graph have recently been shown to provide accurate and diverse recommendations. Nonetheless, these approaches suffer from severe time and space requirements, which can be alleviated via random walk sampling, at the cost of reduced recommendation quality. In addition, these approaches ignore users' ratings, which further limits their expressiveness. In this paper, we introduce a computationally efficient graph-based approach for collaborative filtering based on short-path enumeration. Moreover, we propose three scoring functions based on the Bayesian paradigm that effectively exploit distributional aspects of the users' ratings. We experiment with seven publicly available datasets against state-of-the-art graph-based and matrix factorization approaches. Our empirical results demonstrate the effectiveness of the proposed approach, with significant improvements in most settings. Furthermore, analytical results demonstrate its efficiency compared to other graph-based approaches.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127619960","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}
Chih-Ming Chen, Ming-Feng Tsai, Yu-Ching Lin, Yi-Hsuan Yang
A common scenario considered in recommender systems is to predict a user's preferences on unseen items based on his/her preferences on observed items. A major limitation of this scenario is that a user might be interested in different things each time when using the system, but there is no way to allow the user to actively alter or adjust the recommended results. To address this issue, we propose the idea of "query-based recommendation" that allows a user to specify his/her search intention while exploring new items, thereby incorporating the concept of information retrieval into recommendation systems. Moreover, the idea is more desirable when the user intention can be expressed in different ways. Take music recommendation as an example: the proposed system allows a user to explore new song tracks by specifying either a track, an album, or an artist. To enable such heterogeneous queries in a recommender system, we present a novel technique called "Heterogeneous Preference Embedding" to encode user preference and query intention into low-dimensional vector spaces. Then, with simple search methods or similarity calculations, we can use the encoded representation of queries to generate recommendations. This method is fairly flexible and it is easy to add other types of information when available. Evaluations on three music listening datasets confirm the effectiveness of the proposed method over the state-of-the-art matrix factorization and network embedding methods.
{"title":"Query-based Music Recommendations via Preference Embedding","authors":"Chih-Ming Chen, Ming-Feng Tsai, Yu-Ching Lin, Yi-Hsuan Yang","doi":"10.1145/2959100.2959169","DOIUrl":"https://doi.org/10.1145/2959100.2959169","url":null,"abstract":"A common scenario considered in recommender systems is to predict a user's preferences on unseen items based on his/her preferences on observed items. A major limitation of this scenario is that a user might be interested in different things each time when using the system, but there is no way to allow the user to actively alter or adjust the recommended results. To address this issue, we propose the idea of \"query-based recommendation\" that allows a user to specify his/her search intention while exploring new items, thereby incorporating the concept of information retrieval into recommendation systems. Moreover, the idea is more desirable when the user intention can be expressed in different ways. Take music recommendation as an example: the proposed system allows a user to explore new song tracks by specifying either a track, an album, or an artist. To enable such heterogeneous queries in a recommender system, we present a novel technique called \"Heterogeneous Preference Embedding\" to encode user preference and query intention into low-dimensional vector spaces. Then, with simple search methods or similarity calculations, we can use the encoded representation of queries to generate recommendations. This method is fairly flexible and it is easy to add other types of information when available. Evaluations on three music listening datasets confirm the effectiveness of the proposed method over the state-of-the-art matrix factorization and network embedding methods.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129003081","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}
Peter Brusilovsky, A. Felfernig, P. Lops, J. O'Donovan, G. Semeraro, N. Tintarev, M. Willemsen
As intelligent interactive systems, recommender systems focus on determining predictions that fit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which way the user interface has an influence on the selection behavior of the users. Consequently, it is important to look beyond algorithms. The main goals of the IntRS workshop are to analyze the impact of user interfaces and interaction design, and to explore human interaction with recommender systems. Methodologies for evaluating these aspects are also within the scope of the workshop.
{"title":"RecSys'16 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems","authors":"Peter Brusilovsky, A. Felfernig, P. Lops, J. O'Donovan, G. Semeraro, N. Tintarev, M. Willemsen","doi":"10.1145/2959100.2959199","DOIUrl":"https://doi.org/10.1145/2959100.2959199","url":null,"abstract":"As intelligent interactive systems, recommender systems focus on determining predictions that fit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which way the user interface has an influence on the selection behavior of the users. Consequently, it is important to look beyond algorithms. The main goals of the IntRS workshop are to analyze the impact of user interfaces and interaction design, and to explore human interaction with recommender systems. Methodologies for evaluating these aspects are also within the scope of the workshop.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123267135","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}
Retail Rocket helps web shoppers make better shopping decisions by providing personalized real-time recommendations through multiple channels with over 100MM unique monthly users and 1000+ retail partners. The rapid improvement of the product is important to win on the high-concurrency market of real-time personalization platforms. The necessity of introducing constant innovations and improvements of algorithms for recommendation systems requires correct tools and a process of rapid testing of hypotheses. It's not a secret that 9 out of 10 hypotheses actually do not improve the performance at least. We had the task stated as follows: How to detect and eliminate the idea that doesn't improve as early as possible, to spend a minimum of resources on that process. In the report we will talk about: How we make our process of hypotheses testing faster. One programming language for R&D. Enmity and friendship of offline and online metrics. Why it is difficult to predict the impact of changing diversity of algorithms. What is the benefit of AA/BB online tests. Bayesian statistics for the evaluation of online tests. Roman Zykov is the Chief Data Scientist at the Retail Rocket. In Retail Rocket is responsible for algorithms of personalized and non-personalized recommendations. Previous to Retail Rocket, Roman was the Head of analytics at the biggest e-commerce companies for almost ten years. He received Ms.Sc. in applied mathematics and physics from the MIPhT in 2004.
{"title":"Hypothesis Testing: How to Eliminate Ideas as Soon as Possible","authors":"Roman Zykov","doi":"10.1145/2959100.2959127","DOIUrl":"https://doi.org/10.1145/2959100.2959127","url":null,"abstract":"Retail Rocket helps web shoppers make better shopping decisions by providing personalized real-time recommendations through multiple channels with over 100MM unique monthly users and 1000+ retail partners. The rapid improvement of the product is important to win on the high-concurrency market of real-time personalization platforms. The necessity of introducing constant innovations and improvements of algorithms for recommendation systems requires correct tools and a process of rapid testing of hypotheses. It's not a secret that 9 out of 10 hypotheses actually do not improve the performance at least. We had the task stated as follows: How to detect and eliminate the idea that doesn't improve as early as possible, to spend a minimum of resources on that process. In the report we will talk about: How we make our process of hypotheses testing faster. One programming language for R&D. Enmity and friendship of offline and online metrics. Why it is difficult to predict the impact of changing diversity of algorithms. What is the benefit of AA/BB online tests. Bayesian statistics for the evaluation of online tests. Roman Zykov is the Chief Data Scientist at the Retail Rocket. In Retail Rocket is responsible for algorithms of personalized and non-personalized recommendations. Previous to Retail Rocket, Roman was the Head of analytics at the biggest e-commerce companies for almost ten years. He received Ms.Sc. in applied mathematics and physics from the MIPhT in 2004.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129501825","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}
Tamas Motajcsek, J. Moine, M. Larson, Daniel Kohlsdorf, A. Lommatzsch, D. Tikk, Omar Alonso, P. Cremonesi, Andrew M. Demetriou, Kristaps Dobrajs, F. Garzotto, A. Göker, F. Hopfgartner, D. Malagoli, T. Nguyen, J. Novak, F. Ricci, M. Scriminaci, M. Tkalcic, Anna Zacchi
In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen into and discussing their limitations from a human point of view, we ask the question: if freed from all limitations, what should, and what could, RecSys be? We then turn to the idea that life itself is the best recommender system, and that people themselves are the query. By looking at how life brings people in contact with options that suit their needs or match their preferences, we hope to shed further light on what current RecSys could be doing better. Finally, we look at the forms that RecSys could take in the future. By formulating our vision beyond the reach of usual considerations and current limitations, including business models, algorithms, data sets, and evaluation methodologies, we attempt to arrive at fresh conclusions that may inspire the next steps taken by the community of researchers working on RecSys.
{"title":"Algorithms Aside: Recommendation As The Lens Of Life","authors":"Tamas Motajcsek, J. Moine, M. Larson, Daniel Kohlsdorf, A. Lommatzsch, D. Tikk, Omar Alonso, P. Cremonesi, Andrew M. Demetriou, Kristaps Dobrajs, F. Garzotto, A. Göker, F. Hopfgartner, D. Malagoli, T. Nguyen, J. Novak, F. Ricci, M. Scriminaci, M. Tkalcic, Anna Zacchi","doi":"10.1145/2959100.2959164","DOIUrl":"https://doi.org/10.1145/2959100.2959164","url":null,"abstract":"In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen into and discussing their limitations from a human point of view, we ask the question: if freed from all limitations, what should, and what could, RecSys be? We then turn to the idea that life itself is the best recommender system, and that people themselves are the query. By looking at how life brings people in contact with options that suit their needs or match their preferences, we hope to shed further light on what current RecSys could be doing better. Finally, we look at the forms that RecSys could take in the future. By formulating our vision beyond the reach of usual considerations and current limitations, including business models, algorithms, data sets, and evaluation methodologies, we attempt to arrive at fresh conclusions that may inspire the next steps taken by the community of researchers working on RecSys.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125876119","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}
With the increase of data collected and computation power available, modern recommender systems are ever facing new challenges. While complex models are developed in academia, industry practice seems to focus on relatively simple techniques that can deal with the magnitude of data and the need to distribute the computation. The workshop on large-scale recommender systems (LSRS) is a meeting place for industry and academia to discuss the current and future challenges of applied large-scale recommender systems.
{"title":"LSRS'16: Workshop on Large-Scale Recommender Systems","authors":"Tao Ye, Danny Bickson, Denis Parra","doi":"10.1145/2959100.2959206","DOIUrl":"https://doi.org/10.1145/2959100.2959206","url":null,"abstract":"With the increase of data collected and computation power available, modern recommender systems are ever facing new challenges. While complex models are developed in academia, industry practice seems to focus on relatively simple techniques that can deal with the magnitude of data and the need to distribute the computation. The workshop on large-scale recommender systems (LSRS) is a meeting place for industry and academia to discuss the current and future challenges of applied large-scale recommender systems.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132537168","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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