Recommender System with Machine Learning and Artificial Intelligence

Recommender System with Machine Learning and Artificial Intelligence

Practical Tools and Applications in Medical, Agricultural and Other Industries
1. Aufl.

von: Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Sarika Jain, Ahmed A. Elngar, Priya Gupta

192,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 09.06.2020
ISBN/EAN: 9781119711599
Sprache: englisch
Anzahl Seiten: 448

DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.


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

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