大葉大學圖書館 |

Social network-based recommender systems[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	005.56
書名/作者:	Social network-based recommender systems/ by Daniel Schall.
作者:	Schall, Daniel.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xiii, 126 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Recommender systems (Information filtering)
標題:	Online social networks.
標題:	Computer Science.
標題:	Information Systems Applications (incl. Internet)
標題:	Graph Theory.
標題:	Computer Appl. in Social and Behavioral Sciences.
ISBN:	9783319227351
ISBN:	9783319227344
內容註:	Overview of Social Recommender Systems -- Link Prediction for Directed Graphs -- Follow Recommendation in Communities -- Partner Recommendation -- Social Broker Recommendation -- Conclusion.
摘要、提要註:	This book introduces novel techniques and algorithms necessary to support the formation of social networks. Concepts such as link prediction, graph patterns, recommendation systems based on user reputation, strategic partner selection, collaborative systems and network formation based on 'social brokers' are presented. Chapters cover a wide range of models and algorithms, including graph models and a personalized PageRank model. Extensive experiments and scenarios using real world datasets from GitHub, Facebook, Twitter, Google Plus and the European Union ICT research collaborations serve to enhance reader understanding of the material with clear applications. Each chapter concludes with an analysis and detailed summary. Social Network-Based Recommender Systems is designed as a reference for professionals and researchers working in social network analysis and companies working on recommender systems. Advanced-level students studying computer science, statistics or mathematics will also find this books useful as a secondary text.
電子資源:	http://dx.doi.org/10.1007/978-3-319-22735-1

Social network-based recommender systems[electronic resource] /
Schall, Daniel.

Social network-based recommender systems[electronic resource] /by Daniel Schall. - Cham :Springer International Publishing :2015. - xiii, 126 p. :ill. (some col.), digital ;24 cm.

Overview of Social Recommender Systems -- Link Prediction for Directed Graphs -- Follow Recommendation in Communities -- Partner Recommendation -- Social Broker Recommendation -- Conclusion.

This book introduces novel techniques and algorithms necessary to support the formation of social networks. Concepts such as link prediction, graph patterns, recommendation systems based on user reputation, strategic partner selection, collaborative systems and network formation based on 'social brokers' are presented. Chapters cover a wide range of models and algorithms, including graph models and a personalized PageRank model. Extensive experiments and scenarios using real world datasets from GitHub, Facebook, Twitter, Google Plus and the European Union ICT research collaborations serve to enhance reader understanding of the material with clear applications. Each chapter concludes with an analysis and detailed summary. Social Network-Based Recommender Systems is designed as a reference for professionals and researchers working in social network analysis and companies working on recommender systems. Advanced-level students studying computer science, statistics or mathematics will also find this books useful as a secondary text.

ISBN: 9783319227351

Standard No.: 10.1007/978-3-319-22735-1doiSubjects--Topical Terms:

467318
Recommender systems (Information filtering)

LC Class. No.: QA76.9.I58

Dewey Class. No.: 005.56

Social network-based recommender systems[electronic resource] /
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505 0 $a Overview of Social Recommender Systems -- Link Prediction for Directed Graphs -- Follow Recommendation in Communities -- Partner Recommendation -- Social Broker Recommendation -- Conclusion.
520 $a This book introduces novel techniques and algorithms necessary to support the formation of social networks. Concepts such as link prediction, graph patterns, recommendation systems based on user reputation, strategic partner selection, collaborative systems and network formation based on 'social brokers' are presented. Chapters cover a wide range of models and algorithms, including graph models and a personalized PageRank model. Extensive experiments and scenarios using real world datasets from GitHub, Facebook, Twitter, Google Plus and the European Union ICT research collaborations serve to enhance reader understanding of the material with clear applications. Each chapter concludes with an analysis and detailed summary. Social Network-Based Recommender Systems is designed as a reference for professionals and researchers working in social network analysis and companies working on recommender systems. Advanced-level students studying computer science, statistics or mathematics will also find this books useful as a secondary text.
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