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020 _a9789819725816
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024 7 _a10.1007/978-981-97-2581-6
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
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082 0 4 _a006.3
_223
100 1 _aHu, Lantao.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100746
245 1 0 _aIndustrial Recommender System
_h[electronic resource] :
_bPrinciples, Technologies and Enterprise Applications /
_cby Lantao Hu, Yueting Li, Guangfan Cui, Kexin Yi.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXV, 246 p. 184 illus., 138 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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505 0 _aChapter 1 Introduction to Recommender Systems -- Chapter 2 Content Understanding -- Chapter 3 User Profiles -- Chapter 4 All-encompassing Recall -- Chapter 5 Personalized Ranking -- Chapter 6 Re-consider and Re-rank -- Chapter 7 Cold-start Recommendation -- Chapter 8 Magic Hands in Recommender System -- Chapter 9 AB Testing Platform: A Powerful Tool for System Evolution -- Chapter 10 Advanced Technologies in Recommender System.
520 _aRecommender systems, as a highly popular AI technology in recent years, have been widely applied across various industries. They have transformed the way we interact with technology, influencing our choices and shaping our experiences. This book provides a comprehensive introduction to industrial recommender systems, starting with the overview of the technical framework, gradually delving into each core module such as content understanding, user profiling, recall, ranking, re-ranking and so on, and introducing the key technologies and practices in enterprises. The book also addresses common challenges in recommendation cold start, recommendation bias and debiasing. Additionally, it introduces advanced technologies in the field, such as reinforcement learning, causal inference. Professionals working in the fields of recommender systems, computational advertising, and search will find this book valuable. It is also suitable for undergraduate, graduate, and doctoral students majoring in artificial intelligence, computer science, software engineering, and related disciplines. Furthermore, it caters to readers with an interest in recommender systems, providing them with an understanding of the foundational framework, insights into core technologies, and advancements in industrial recommender systems. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aExpert systems (Computer science).
_93392
650 0 _aArtificial intelligence
_xData processing.
_921787
650 0 _aData mining.
_93907
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aKnowledge Based Systems.
_979172
650 2 4 _aData Science.
_934092
650 2 4 _aData Mining and Knowledge Discovery.
_9100748
700 1 _aLi, Yueting.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100750
700 1 _aCui, Guangfan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100751
700 1 _aYi, Kexin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100753
710 2 _aSpringerLink (Online service)
_9100756
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819725809
776 0 8 _iPrinted edition:
_z9789819725823
776 0 8 _iPrinted edition:
_z9789819725830
856 4 0 _uhttps://doi.org/10.1007/978-981-97-2581-6
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