000			06055cam a2200685 i 4500
001			on1159628189
003			OCoLC
005			20240523125542.0
006			m o d
007			cr cnu---unuuu
008			200531s2020 nju ob 001 0 eng
010			_a 2020024765
040			_aDLC _beng _erda _epn _cDLC _dDG1 _dOCLCF _dN$T _dOCLCQ _dYDX _dOCLCA _dOCLCO _dOCLCQ _dUPM _dOCLCQ _dOCLCO
020			_a9781119711582 _qelectronic book
020			_a1119711584 _qelectronic book
020			_a9781119711605 _qelectronic book
020			_a1119711606 _qelectronic book
020			_a9781119711599 _qelectronic book
020			_a1119711592 _qelectronic book
020			_z9781119711575 _qhardcover
029	1		_aAU@ _b000067284790
029	1		_aCHNEW _b001089870
029	1		_aCHVBK _b600431908
035			_a(OCoLC)1159628189
042			_apcc
050	0	0	_aZA3084 _b.R43 2020
082	0	0	_a025.04 _223
049			_aMAIN
245	0	0	_aRecommender system with machine learning and artificial intelligence : _bpractical tools and applications in medical, agricultural and other industries / _cedited by Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Sarika Jain, Ahmed A. Elngar and Priya Gupta.
264		1	_aHoboken, NJ : _bJohn Wiley & Sons, Inc., _c[2020]
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	0		_aMachine learning in biomedical science and healthcare informatics
504			_aIncludes bibliographical references and index.
505	0		_aAn introduction to basic concepts on recommender systems / Pooja Rana, Nishi Jain and Usha Mittal -- A brief model overview of personalized recommendation to citizens in the health-care industry / Subhasish Mohapatra and Kunal Anand -- 2Es of TIS : a review of information exchange and extraction in tourism information systems / Malik M. Saad Missen, Micka�el Coustaty, Hina Asmat, Amnah Firdous, Nadeem Akhtar, Muhammad Akram and V.B. Surya Prasath -- Concepts of recommendation system from the perspective of machine learning / Sumanta Chandra Mishra Sharma, Adway Mitra and Deepayan Chakraborty -- A machine learning approach to recommend suitable crops and fertilizers for agriculture / Govind Kumar Jha, Preetish Ranjan and Manish Gaur -- Accuracy-assured privacy-preserving recommender system using hybrid-based deep learning method / Abhaya Kumar Sahoo and Chittaranjan Pradhan -- Machine learning-based recommender system for breast cancer prognosis / G. Kanimozhi, P. Shanmugavadivu and M. Mary Shanthi Rani -- A recommended system for crop disease detection and yield prediction using machine learning approach / Pooja Akulwar -- Content-based recommender systems / Poonam Bhatia Anand and Rajender Nath -- Content (item)-based recommendation system / R. Balamurali -- Content-based health recommender systems / Soumya Prakash Rana, Maitreyee Dey, Javier Prieto and Sandra Dudley -- Context-based social media recommendation system / R. Sujithra Kanmani and B. Surendiran -- Netflix challenge : improving movie recommendations / Vasu Goel -- Product or item-based recommender system / Jyoti Rani, Usha Mittal and Geetika Gupta -- A trust-based recommender system built on IoT blockchain network with cognitive framework / S. Porkodi and D. Kesavaraja -- Development of a recommender system HealthMudra using blockchain for prevention of diabetes / Rashmi Bhardwaj and Debabrata Datta -- Case study 1 : health care recommender systems / Usha Mittal, Nancy Singla and Geetika Gupta -- Temporal change analysis-based recommender system for Alzheimer Disease classification / S. Naganandhini, P. Shanmugavadivu and M. Mary Shanthi Rani -- Regularization of graphs : sentiment classification / R.S.M. Lakshmi Patibandla -- TSARS : a tree-similarity algorithm-based agricultural recommender system / Madhusree Kuanr, Puspanjali Mohapatra and Sasmita Subhadarsinee Choudhury -- Influenceable targets recommendation analyzing social activities in egocentric online social networks / Soumyadeep Debnath, Dhrubasish Sarkar and Dipankar Das.
520			_a"The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments"-- _cProvided by publisher
588			_aDescription based on online resource; title from digital title page (viewed on February 02, 2021).
590			_aJohn Wiley and Sons _bWiley Online Library: Complete oBooks
650		0	_aRecommender systems (Information filtering)
650		0	_aMachine learning.
650		0	_aArtificial intelligence.
650		6	_aSyst�emes de recommandation (Filtrage d'information)
650		6	_aApprentissage automatique.
650		6	_aIntelligence artificielle.
650		7	_aartificial intelligence. _2aat
650		7	_aArtificial intelligence _2fast
650		7	_aMachine learning _2fast
650		7	_aRecommender systems (Information filtering) _2fast
700	1		_aMohanty, Sachi Nandan, _eeditor.
700	1		_aChatterjee, Jyotir Moy, _eeditor.
700	1		_aJain, Sarika, _eeditor.
700	1		_aElngar, Ahmed A., _eeditor.
700	1		_aGupta, Priya _c(Professor of computer science), _eeditor.
776	0	8	_iPrint version: _tRecommender system with machine learning and artificial intelligence. _dHoboken, NJ : Wiley-Scrivener, 2020 _z9781119711575 _w(DLC) 2020024764
856	4	0	_uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781119711582
938			_aEBSCOhost _bEBSC _n2497252
994			_a92 _bINLUM
999			_c12716 _d12716