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E-raamat: Recommender System with Machine Learning and Artif icial Intelligence: Practical Tools and Applicatio ns in Medical, Agricultural and Other Industries: Practical Tools and Applications in Medical, Agricultural and Other Industries [Wiley Online]

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  • Formaat: 448 pages
  • Ilmumisaeg: 01-Sep-2020
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119711584
  • ISBN-13: 9781119711582
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  • Wiley Online
  • Hind: 250,53 €*
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Recommender System with Machine Learning and Artif icial Intelligence: Practical Tools and Applicatio ns in Medical, Agricultural and Other Industries: Practical Tools and Applications in Medical, Agricultural and Other IndustriesSuurem pilt
  • Formaat: 448 pages
  • Ilmumisaeg: 01-Sep-2020
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119711584
  • ISBN-13: 9781119711582
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119711582

This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior.  It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior.  Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising.

This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.

Preface xix
Acknowledgment xxiii
Part 1 Introduction to Recommender Systems
1(70)
1 An Introduction to Basic Concepts on Recommender Systems
3(24)
Pooja Rana
Nishi Jain
Usha Mittal
1.1 Introduction
4(1)
1.2 Functions of Recommendation Systems
5(1)
1.3 Data and Knowledge Sources
6(2)
1.4 Types of Recommendation Systems
8(6)
1.4.1 Content-Based
8(3)
1.4.1.1 Advantages of Content-Based Recommendation
11(1)
1.4.1.2 Disadvantages of Content-Based Recommendation
11(1)
1.4.2 Collaborative Filtering
12(2)
1.5 Item-Based Recommendation vs. User-Based Recommendation System
14(5)
1.5.1 Advantages of Memory-Based Collaborative Filtering
15(1)
1.5.2 Shortcomings
16(1)
1.5.3 Advantages of Model-Based Collaborative Filtering
17(1)
1.5.4 Shortcomings
17(1)
1.5.5 Hybrid Recommendation System
17(1)
1.5.6 Advantages of Hybrid Recommendation Systems
18(1)
1.5.7 Shortcomings
18(1)
1.5.8 Other Recommendation Systems
18(1)
1.6 Evaluation Metrics for Recommendation Engines
19(1)
1.7 Problems with Recommendation Systems and Possible Solutions
20(4)
1.7.1 Advantages of Recommendation Systems
23(1)
1.7.2 Disadvantages of Recommendation Systems
24(1)
1.8 Applications of Recommender Systems
24(3)
References
25(2)
2 A Brief Model Overview of Personalized Recommendation to Citizens in the Health-Care Industry
27(18)
Subhasish Mohapatra
Kunal Anand
2.1 Introduction
28(1)
2.2 Methods Used in Recommender System
29(4)
2.2.1 Content-Based
29(3)
2.2.2 Collaborative Filtering
32(1)
2.2.3 Hybrid Filtering
33(1)
2.3 Related Work
33(1)
2.4 Types of Explanation
34(1)
2.5 Explanation Methodology
35(4)
2.5.1 Collaborative-Based
36(1)
2.5.2 Content-Based
36(1)
2.5.3 Knowledge and Utility-Based
37(1)
2.5.4 Case-Based
37(1)
2.5.5 Demographic-Based
38(1)
2.6 Proposed Theoretical Framework for Explanation-Based Recommender System in Health-Care Domain
39(1)
2.7 Flowchart
39(2)
2.8 Conclusion
41(4)
References
41(4)
3 2Es of TIS: A Review of Information Exchange and Extraction in Tourism Information Systems
45(26)
Malik M. Saad Missen
Mickael Coustaty
Hina Asmat
Amnah Firdous
Nadeem Akhtar
Muhammad Akram
V. B. Surya Prasath
3.1 Introduction
46(3)
3.2 Information Exchange
49(6)
3.2.1 Exchange of Tourism Objects Data
49(1)
3.2.1.1 Semantic Clashes
50(1)
3.2.1.2 Structural Clashes
50(1)
3.2.2 Schema.org---The Future
51(1)
3.2.2.1 Schema.org Extension Mechanism
52(1)
3.2.2.2 Schema.org Tourism Vocabulary
52(1)
3.2.3 Exchange of Tourism-Related Statistical Data
53(2)
3.3 Information Extraction
55(2)
3.3.1 Opinion Extraction
56(1)
3.3.2 Opinion Mining
57(1)
3.4 Sentiment Annotation
57(5)
3.4.1 SentiML
58(1)
3.4.1.1 SentiML Example
58(1)
3.4.2 OpinionMiningML
59(1)
3.4.2.1 OpinionMiningML Example
60(1)
3.4.3 EmotionML
61(1)
3.4.3.1 EmotionML Example
61(1)
3.5 Comparison of Different Annotations Schemes
62(2)
3.6 Temporal and Event Extraction
64(1)
3.7 TimeML
65(2)
3.8 Conclusions
67(4)
References
67(4)
Part 2 Machine Learning-Based Recommender Systems
71(94)
4 Concepts of Recommendation System from the Perspective of Machine Learning
73(16)
Sumanta Chandra Mishra Sharma
Adway Mitra
Deepayan Chakraborty
4.1 Introduction
73(1)
4.2 Entities of Recommendation System
74(2)
4.2.1 User
74(1)
4.2.2 Items
75(1)
4.2.3 Action
75(1)
4.3 Techniques of Recommendation
76(6)
4.3.1 Personalized Recommendation System
77(1)
4.3.2 Non-Personalized Recommendation System
77(1)
4.3.3 Content-Based Filtering
77(1)
4.3.4 Collaborative Filtering
78(2)
4.3.5 Model-Based Filtering
80(1)
4.3.6 Memory-Based Filtering
80(1)
4.3.7 Hybrid Recommendation Technique
81(1)
4.3.8 Social Media Recommendation Technique
82(1)
4.4 Performance Evaluation
82(1)
4.5 Challenges
83(2)
4.5.1 Sparsityof Data
84(1)
4.5.2 Scalability
84(1)
4.5.3 Slow Start
84(1)
4.5.4 Gray Sheep and Black Sheep
84(1)
4.5.5 Item Duplication
84(1)
4.5.6 Privacy Issue
84(1)
4.5.7 Biasness
85(1)
4.6 Applications
85(1)
4.7 Conclusion
85(4)
References
85(4)
5 A Machine Learning Approach to Recommend Suitable Crops and Fertilizers for Agriculture
89(12)
Govind Kumar Jha
Preetish Ranjan
Manish Gaur
5.1 Introduction
90(1)
5.2 Literature Review
91(2)
5.3 Methodology
93(3)
5.4 Results and Analysis
96(1)
5.5 Conclusion
97(4)
References
98(3)
6 Accuracy-Assured Privacy-Preserving Recommender System Using Hybrid-Based Deep Learning Method
101(20)
Abhaya Kumar Sahoo
Chittaranjan Pradhan
6.1 Introduction
102(1)
6.2 Overview of Recommender System
103(3)
6.3 Collaborative Filtering-Based Recommender System
106(1)
6.4 Machine Learning Methods Used in Recommender System
107(3)
6.5 Proposed RBM Model-Based Movie Recommender System
110(3)
6.6 Proposed CRBM Model-Based Movie Recommender System
113(2)
6.7 Conclusion and Future Work
115(6)
References
118(3)
7 Machine Learning-Based Recommender System for Breast Cancer Prognosis
121(20)
G. Kanimozhi
P. Shanmugavadivu
M. Mary Shanthi Rani
7.1 Introduction
122(2)
7.2 Related Works
124(1)
7.3 Methodology
125(6)
7.3.1 Experimental Dataset
125(2)
7.3.2 Feature Selection
127(1)
7.3.3 Functional Phases of MLRS-BC
128(1)
7.3.4 Prediction Algorithms
129(2)
7.4 Results and Discussion
131(7)
7.5 Conclusion
138(3)
Acknowledgment
139(1)
References
139(2)
8 A Recommended System for Crop Disease Detection and Yield Prediction Using Machine Learning Approach
141(24)
Pooja Akulwar
8.1 Introduction
142(1)
8.2 Machine Learning
143(8)
8.2.1 Overview
143(2)
8.2.2 Machine Learning Algorithms
145(1)
8.2.3 Machine Learning Methods
146(1)
8.2.3.1 Artificial Neural Network
146(1)
8.2.3.2 Support Vector Machines
146(1)
8.2.3.3 K-Nearest Neighbors (K-NN)
147(1)
8.2.3.4 Decision Tree Learning
147(1)
8.2.3.5 Random Forest
148(1)
8.2.3.6 Gradient Boosted Decision Tree (GBDT)
149(1)
8.2.3.7 Regularized Greedy Forest (RGF)
150(1)
8.3 Recommender System
151(2)
8.3.1 Overview
151(2)
8.4 Crop Management
153(6)
8.4.1 Yield Prediction
153(1)
8.4.2 Disease Detection
154(2)
8.4.3 Weed Detection
156(3)
8.4.4 Crop Quality
159(1)
8.5 Application---Crop Disease Detection and Yield Prediction
159(6)
References
162(3)
Part 3 Content-Based Recommender Systems
165(126)
9 Content-Based Recommender Systems
167(30)
Poonam Bhatia Anand
Rajender Nath
9.1 Introduction
167(1)
9.2 Literature Review
168(4)
9.3 Recommendation Process
172(4)
9.3.1 Architecture of Content-Based Recommender System
172(3)
9.3.2 Profile Cleaner Representation
175(1)
9.4 Techniques Used for Item Representation and Learning User Profile
176(6)
9.4.1 Representation of Content
176(1)
9.4.2 Vector Space Model Based on Keywords
177(2)
9.4.3 Techniques for Learning Profiles of User
179(1)
9.4.3.1 Probabilistic Method
179(1)
9.4.3.2 Rocchio's and Relevance Feedback Method
180(1)
9.4.3.3 Other Methods
181(1)
9.5 Applicability of Recommender System in Healthcare and Agriculture
182(4)
9.5.1 Recommendation System in Healthcare
182(2)
9.5.2 Recommender System in Agriculture
184(2)
9.6 Pros and Cons of Content-Based Recommender System
186(1)
9.7 Conclusion
187(10)
References
188(9)
10 Content (Item)-Based Recommendation System
197(18)
R. Balamurali
10.1 Introduction
198(1)
10.2 Phases of Content-Based Recommendation Generation
198(1)
10.3 Content-Based Recommendation Using Cosine Similarity
199(5)
10.4 Content-Based Recommendations Using Optimization Techniques
204(4)
10.5 Content-Based Recommendation Using the Tree Induction Algorithm
208(4)
10.6 Summary
212(3)
References
213(2)
11 Content-Based Health Recommender Systems
215(22)
Soumya Prakash Rana
Maitreyee Dey
Javier Prieto
Sandra Dudley
11.1 Introduction
216(1)
11.2 Typical Health Recommender System Framework
217(1)
11.3 Components of Content-Based Health Recommender System
218(2)
11.4 Unstructured Data Processing
220(1)
11.5 Unsupervised Feature Extraction & Weighting
221(1)
11.5.1 Bag of Words (BoW)
221(1)
11.5.2 Word to Vector (Word2Vec)
222(1)
11.5.3 Global Vectors for Word Representations (Glove)
222(1)
11.6 Supervised Feature Selection & Weighting
222(3)
11.7 Feedback Collection
225(1)
11.7.1 Medication & Therapy
225(1)
11.7.2 Healthy Diet Plan
225(1)
11.7.3 Suggestions
225(1)
11.8 Training & Health Recommendation Generation
226(2)
11.8.1 Analogy-Based ML in CBHRS
227(1)
11.8.2 Specimen-Based ML in CBHRS
227(1)
11.9 Evaluation of Content Based Health Recommender System
228(1)
11.10 Design Criteria of CBHRS
229(2)
11.10.1 Micro-Level 8c Lucidity
230(1)
11.10.2 Interactive Interface
230(1)
11.10.3 Data Protection
230(1)
11.10.4 Risk & Uncertainty Management
231(1)
11.10.5 Doctor-in-Loop (DiL)
231(1)
11.11 Conclusions and Future Research Directions
231(6)
References
233(4)
12 Context-Based Social Media Recommendation System
237(14)
R. Sujithra Kanmani
B. Surendiran
12.1 Introduction
237(3)
12.2 Literature Survey
240(1)
12.3 Motivation and Objectives
241(2)
12.3.1 Architecture
241(1)
12.3.2 Modules
242(1)
12.3.3 Implementation Details
243(1)
12.4 Performance Measures
243(1)
12.5 Precision
243(1)
12.6 Recall
243(1)
12.7 F-Measure
244(1)
12.8 Evaluation Results
244(3)
12.9 Conclusion and Future Work
247(4)
References
248(3)
13 Netflix Challenge---Improving Movie Recommendations
251(18)
Vasu Goel
13.1 Introduction
251(1)
13.2 Data Preprocessing
252(1)
13.3 MovieLens Data
253(2)
13.4 Data Exploration
255(1)
13.5 Distributions
256(1)
13.6 Data Analysis
257(8)
13.7 Results
265(1)
13.8 Conclusion
266(3)
References
266(3)
14 Product or Item-Based Recommender System
269(22)
Jyoti Rani
Usha Mittal
Geetika Gupta
14.1 Introduction
270(1)
14.2 Various Techniques to Design Food Recommendation System
271(5)
14.2.1 Collaborative Filtering Recommender Systems
271(1)
14.2.2 Content-Based Recommender Systems (CB)
272(1)
14.2.3 Knowledge-Based Recommender Systems
272(1)
14.2.4 Hybrid Recommender Systems
273(1)
14.2.5 Context Aware Approaches
273(1)
14.2.6 Group-Based Methods
273(1)
14.2.7 Different Types of Food Recommender Systems
273(3)
14.3 Implementation of Food Recommender System Using Content-Based Approach
276(6)
14.3.1 Item Profile Representation
277(1)
14.3.2 Information Retrieval
278(1)
14.3.3 Word2vec
278(1)
14.3.4 How are word2vec Embedding's Obtained?
278(1)
14.3.5 Obtaining word2vec Embeddings
279(1)
14.3.6 Dataset
280(1)
14.3.6.1 Data Preprocessing
280(1)
14.3.7 Web Scrapping For Food List
280(1)
14.3.7.1 Porter Stemming All Words
280(1)
14.3.7.2 Filtering Our Ingredients
280(1)
14.3.7.3 Final Data Frame with Dishes and Their Ingredients
281(1)
14.3.7.4 Hamming Distance
281(1)
14.3.7.5 Jaccard Distance
282(1)
14.4 Results
282(1)
14.5 Observations
283(1)
14.6 Future Perspective of Recommender Systems
283(3)
14.6.1 User Information Challenges
283(1)
14.6.1.1 User Nutrition Information Uncertainty
283(1)
14.6.1.2 User Rating Data Collection
284(1)
14.6.2 Recommendation Algorithms Challenges
284(1)
14.6.2.1 User Information Such as Likes/Dislikes Food or Nutritional Needs
284(1)
14.6.2.2 Recipe Databases
284(1)
14.6.2.3 A Set of Constraints or Rules
285(1)
14.6.3 Challenges Concerning Changing Eating Behavior of Consumers
285(1)
14.6.4 Challenges Regarding Explanations and Visualizations
286(1)
14.7 Conclusion
286(5)
Acknowledgements
287(1)
References
287(4)
Part 4 Blockchain & IoT-Based Recommender Systems
291(38)
15 A Trust-Based Recommender System Built on IoT Blockchain Network With Cognitive Framework
293(20)
S. Porkodi
D. Kesavaraja
15.1 Introduction
294(3)
15.1.1 Today and Tomorrow
294(1)
15.1.2 Vision
294(1)
15.1.3 Internet of Things
294(1)
15.1.4 Blockchain
295(1)
15.1.5 Cognitive Systems
296(1)
15.1.6 Application
296(1)
15.2 Technologies and its Combinations
297(2)
15.2.1 IoT--Blockchain
297(1)
15.2.2 IoT--Cognitive System
298(1)
15.2.3 Blockchain--Cognitive System
298(1)
15.2.4 IoT--Blockchain--Cognitive System
298(1)
15.3 Crypto Currencies With IoT--Case Studies
299(1)
15.4 Trust-Based Recommender System
299(5)
15.4.1 Requirement
299(3)
15.4.2 Things Management
302(1)
15.4.3 Cognitive Process
303(1)
15.5 Recommender System Platform
304(3)
15.6 Conclusion and Future Directions
307(6)
References
307(6)
16 Development of a Recommender System HealthMudra Using Blockchain for Prevention of Diabetes
313(16)
Rashmi Bhardwaj
Debabrata Datta
16.1 Introduction
314(3)
16.2 Architecture of Blockchain
317(5)
16.2.1 Definition of Blockchain
318(1)
16.2.2 Structure of Blockchain
318(4)
16.3 Role of HealthMudra in Diabetic
322(2)
16.4 Blockchain Technology Solutions
324(1)
16.4.1 Predictive Models of Health Data Analysis
325(1)
16.5 Conclusions
325(4)
References
326(3)
Part 5 Healthcare Recommender Systems
329(88)
17 Case Study 1: Health Care Recommender Systems
331(20)
Usha Mittal
Nancy Singla
Geetika Gupta
17.1 Introduction
332(3)
17.1.1 Health Care Recommender System
332(1)
17.1.2 Parkinson's Disease: Causes and Symptoms
333(1)
17.1.3 Parkinson's Disease: Treatment and Surgical Approaches
334(1)
17.2 Review of Literature
335(6)
17.2.1 Machine Learning Algorithms for Parkinson's Data
337(3)
17.2.2 Visualization
340(1)
17.3 Recommender System for Parkinson's Disease (PD)
341(4)
17.3.1 How Will One Know When Parkinson's has Progressed?
342(1)
17.3.2 Dataset for Parkinson's Disease (PD)
342(1)
17.3.3 Feature Selection
343(1)
17.3.4 Classification
343(1)
17.3.4.1 Logistic Regression
343(1)
17.3.4.2 K Nearest Neighbor (KNN)
343(1)
17.3.4.3 Support Vector Machine (SVM)
344(1)
17.3.4.4 Decision Tree
344(1)
17.3.5 Train and Test Data
344(1)
17.3.6 Recommender System
344(1)
17.4 Future Perspectives
345(1)
17.5 Conclusions
346(5)
References
348(3)
18 Temporal Change Analysis-Based Recommender System for Alzheimer Disease Classification
351(22)
S. Naganandhini
P. Shanmugavadivu
M. Mary Shanthi Rani
18.1 Introduction
352(1)
18.2 Related Work
352(1)
18.3 Mechanism of TCA-RS-AD
353(1)
18.4 Experimental Dataset
354(3)
18.5 Neural Network
357(13)
18.6 Conclusion
370(3)
References
370(3)
19 Regularization of Graphs: Sentiment Classification
373(14)
R.S.M. Lakshmi Patibandla
19.1 Introduction
373(1)
19.2 Neural Structured Learning
374(1)
19.3 Some Neural Network Models
375(2)
19.4 Experimental Results
377(6)
19.4.1 Base Model
379(3)
19.4.2 Graph Regularization
382(1)
19.5 Conclusion
383(4)
References
384(3)
20 TSARS: A Tree-Similarity Algorithm-Based Agricultural Recommender System
387(14)
Madhusree Kuanr
Puspanjali Mohapatra
Sasmita Subhadarsinee Choudhury
20.1 Introduction
388(2)
20.2 Literature Survey
390(3)
20.3 Research Gap
393(1)
20.4 Problem Definitions
393(1)
20.5 Methodology
393(1)
20.6 Results & Discussion
394(3)
20.6.1 Performance Evaluation
394(2)
20.6.2 Time Complexity Analysis
396(1)
20.7 Conclusion & Future Work
397(4)
References
399(2)
21 Influenceable Targets Recommendation Analyzing Social Activities in Egocentric Online Social Networks
401(16)
Soumyadeep Debnath
Dhrubasish Sarkar
Dipankar Das
21.1 Introduction
402(1)
21.2 Literature Review
403(1)
21.3 Dataset Collection Process with Details
404(2)
21.3.1 Main User's Activities Data
405(1)
21.3.2 Network Member's Activities Data
405(1)
21.3.3 Tools and Libraries for Data Collection
405(1)
21.3.4 Details of the Datasets
406(1)
21.4 Primary Preprocessing of Data
406(1)
21.4.1 Language Detection and Translation
406(1)
21.4.2 Tagged Tweeters Collection
407(1)
21.4.3 Textual Noise Removal
407(1)
21.4.4 Textual Spelling and Correction
407(1)
21.5 Influence and Social Activities Analysis
407(2)
21.5.1 Step 1: Targets Selection From OSMs
408(1)
21.5.2 Step 3: Categories Classification of Social Contents
408(1)
21.5.3 Step 4: Sentiments Analysis of Social Contents
408(1)
21.6 Recommendation System
409(4)
21.6.1 Secondary Preprocessing of Data
409(2)
21.6.2 Recommendation Analyzing Contents of Social Activities
411(2)
21.7 Top Most Influenceable Targets Evaluation
413(1)
21.8 Conclusion
414(1)
21.9 Future Scope
415(2)
References
415(2)
Index 417
Sachi Nandan Mohanty received his PhD from IIT Kharagpur, India in 2015 and is now at ICFAI Foundation for Higher Education, Hyderabad, India.

Jyotir Moy Chatterjee is working as an Assistant Professor (IT) at Lord Buddha Education Foundation, Kathmandu, Nepal. He has completed M.Tech in Computer Science & Engineering from Kalinga Institute of Industrial Technology, Bhubaneswar, India.

Sarika Jain obtained her PhD in the field of Knowledge Representation in Artificial Intelligence in 2011. She has served in the field of education for over 18 years and is currently in service at the National Institute of Technology, Kurukshetra.

Ahmed A. Elngar is the Founder and Head of Scientific Innovation Research Group (SIRG) and Assistant Professor of Computer Science at the Faculty of Computers and Information, Beni-Suef University, Egypt.

Priya Gupta is working as an Assistant Professor in the Department of Computer Science at Maharaja Agrasen College, University of Delhi. Her Doctoral Degree is from BIT (Mesra), Ranchi.
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