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000755507 020__ $$a9789811007484$$q(electronic book)
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000755507 050_4 $$aQA76.9.I58
000755507 08204 $$a005.5/6$$223
000755507 1001_ $$aYin, Hongzhi,$$eauthor.
000755507 24510 $$aSpatio-temporal recommendation in social media$$h[electronic resource] /$$cHongzhi Yin, Bin Cui.
000755507 264_1 $$aSingapore :$$bSpringer,$$c2016.
000755507 300__ $$a1 online resource (xiii, 114 pages) :$$billustrations.
000755507 336__ $$atext$$btxt$$2rdacontent
000755507 337__ $$acomputer$$bc$$2rdamedia
000755507 338__ $$aonline resource$$bcr$$2rdacarrier
000755507 4901_ $$aSpringerBriefs in computer science,$$x2191-5768
000755507 504__ $$aIncludes bibliographical references.
000755507 5050_ $$a1. Introduction -- 2. Temporal Context-Aware Recommendation -- 3. Spatial Context-Aware Recommendation -- 4. Location-based and Real-time Recommendation -- 5. Fast Online Recommendation. .
000755507 506__ $$aAccess limited to authorized users.
000755507 520__ $$aThis book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users' behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users' behaviors, user interest drift over geographical regions, data sparsity and cold start. Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students.
000755507 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed May 26, 2016).
000755507 650_0 $$aRecommender systems (Information filtering)
000755507 650_0 $$aSocial media.
000755507 7001_ $$aCui, Bin,$$eauthor
000755507 77608 $$iPrint version:$$aYin, Hongzhi.$$tSpatio-temporal recommendation in social media.$$dSingapore : Springer, c2016$$z9789811007477$$w(DLC)  2016939068
000755507 830_0 $$aSpringerBriefs in computer science.
000755507 852__ $$bebk
000755507 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-10-0748-4$$zOnline Access$$91397441.1
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000755507 980__ $$aEBOOK
000755507 980__ $$aBIB
000755507 982__ $$aEbook
000755507 983__ $$aOnline
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