Recommender Systems: Algorithms and Applications

✍ Scribed by P. Pavan Kumar; S. Vairachilai; Sirisha Potluri; Sachi Nandan Mohanty

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 249
• Category: Library

✦ Synopsis

Recommender systems use information filtering to predict user preferences. They are becoming a vital part of e-business and are used in a wide variety of industries, ranging from entertainment and social networking to information technology, tourism, education, agriculture, healthcare, manufacturing, and retail. Recommender Systems: Algorithms and Applications dives into the theoretical underpinnings of these systems and looks at how this theory is applied and implemented in actual systems.

The book examines several classes of recommendation algorithms, including

• Machine learning algorithms
• Community detection algorithms
• Filtering algorithms

Various efficient and robust product recommender systems using machine learning algorithms are helpful in filtering and exploring unseen data by users for better prediction and extrapolation of decisions. These are providing a wider range of solutions to such challenges as imbalanced data set problems, cold-start problems, and long tail problems. This book also looks at fundamental ontological positions that form the foundations of recommender systems and explain why certain recommendations are predicted over others.

Techniques and approaches for developing recommender systems are also investigated. These can help with implementing algorithms as systems and include

• A latent-factor technique for model-based filtering systems
• Collaborative filtering approaches
• Content-based approaches

Finally, this book examines actual systems for social networking, recommending consumer products, and predicting risk in software engineering projects.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgements
Editors
List of Contributors
Chapter 1: Collaborative Filtering-based Robust Recommender System using Machine Learning Algorithms
1.1 Introduction
1.2 The Background of the Recommender System
1.3 Types of Filtering
1.3.1 Content-Based Technique
1.3.2 Collaborative-Based Technique
1.3.3 Hybrid-Based Technique
1.3.4 Pros of Collaborative Filtering
1.3.5 Cons of Collaborative Filtering
1.4 Different methods for Recommendation systems
1.4.1 K-Nearest Neighbors (KNN)
1.4.2 Support Vector Machine (SVM)
1.4.3 Random Decision Trees/Random Forest
1.4.4 Decision Tree
1.5 Support Vector Machine
1.6 KNN
1.6.1 Algorithm
1.6.2 Curse of Dimensionality
1.7 Random Forest
1.7.1 Random Forest Classifier
1.7.2 Mathematical Expression
1.7.2.1 Node Impurity/Impurity Criterion
1.7.2.2 Information Gain
1.7.2.3 Implementation in scikit-learn
1.7.2.4 Implementation in Spark
1.8 Comparison of Algorithms
1.9 Limitation and Challenges
1.9.1 Data Sparsity
1.9.2 Scalability
1.9.3 Cold Start Problem
1.10 Future Scope
1.10.1 Medical
1.10.2 Domain Adaption
1.10.3 Data Sparseness & Cold Start
1.11 Conclusion
1.12 Contribution
References
Chapter 2: An Experimental Analysis of Community Detection Algorithms on a Temporally Evolving Dataset
2.1 Introduction
2.2 Background
2.2.1 Community Detection
2.2.2 Community Detection Approach
2.2.2.1 Label Propagation
2.2.2.2 Walktrap
2.2.2.3 Spinglass
2.2.2.4 Infomap
2.2.2.5 Eigenvector
2.2.2.6 Edge Betweenness
2.2.2.7 Multilevel
2.2.2.8 Fast Greedy
2.2.2.9 Louvain
2.2.3 Gaps in Existing Community Detection Algorithms
2.3 Experimental Study
2.3.1 Framework
2.3.2 Result Analysis
2.3.2.1 RQ1. Are the Existing CD Algorithms Suitable for Evolving SNs?
2.3.2.2 RQ 2. Are the LFR Benchmark Parameters Suitable for Comparing CD Algorithms?
2.3.2.3 RQ 3. If So, Which Metrics are More Significant for Evolving SNs?
2.3.2.3.1 The rank of Algorithm
2.3.3 Threats to Validity
2.4 Conclusion and Future Work
Note
References
Chapter 3: Why This Recommendation?: Explainable Product Recommendations with Ontological Knowledge Reasoning
3.1 Introduction
3.2 Related Works
3.3 Motivation
3.4 Recommending Products Using Case-Based Reasoning
3.4.1 Extracting Product Aspects
3.4.2 Evaluating Aspect Opinions and Sentiments
3.4.3 Generation of Base Cases and Query Case
3.4.4 Recommending Products Using Sentiment and Similarity between Query Case and Base Cases
3.5 Explanations to the Product Recommendations by Ontological Reasoning
3.6 Experimental Setup and Results Discussion
3.7 Conclusion and Future Scope
References
Chapter 4: Model-based Filtering Systems using a Latent-factor Technique
4.1 Introduction
4.2 Types of Model-Based Filtering
4.2.1 Clustering Collaborating Filtering
4.2.2 Classification Collaborating Filtering
4.2.3 Markov Decision Process (MDP)-Based CF
4.2.4 Latent Factor
4.3 The Geometrical Intuition Behind the Latent Factor Model
4.4 Some Fundamental Matrix Factorization Concepts
4.4.1 Unconstrained Matrix Factorization
4.4.2 Single Value Decomposition
4.4.3 Non-Negative Matrix Factorization
4.5 Understanding the Matrix Family
4.6 Integrating Factorization and Neighbourhood Model
4.6.1 Baseline Estimator: A Personalized Bias-Centric Model
4.6.2 Neighborhood Portion Model
4.6.3 Latent Factor Portion Model
4.6.4 Integration of the Latent Factor Model and the Neighbourhood Portion Model
4.6.5 Solving the Optimization Model
4.6.6 Perception about Accuracy
4.6.7 Integration of Latent Factor Models with Arbitrary Models
4.7 Conclusion
References
Chapter 5: Recommender Systems for the Social Networking Context for Collaborative Filtering and Content-Based Approaches
5.1 Introduction
5.2 Traditional and Latest Systems Recommender
5.2.1 Collaborative Scanning and the Scanning of Content
5.2.2 Partnership Screening
5.2.3 Material-Dependent Screening
5.2.4 Recommendation System Gaps
5.2.4.1 Dark Drop
5.2.4.2 Late Determination
5.2.4.3 Evaluations Sparse
5.2.4.4 High Specification Specialization
5.2.5 Evaluation Methods in Recommender Systems
5.2.6 Precision, Recall, and Fall-Out
5.3 Educational Institutions and Social Networks
5.4 Model of the User of Scientific Recommender
5.5 Conclusions
References
Chapter 6: Recommendation System for Risk Assessment in Requirements Engineering of Software with Tropos Goal–Risk Model
6.1 Introduction
6.2 Related Work
6.3 Problem Formulation
6.4 Proposed Goal-Based Risk Analysis
6.4.1 Probablistic Risk Analysis (PRA) Algorithm
6.4.2 Dataset Description
6.5 Implementation and Results
6.6 Conclusion and Future Work
References
Chapter 7: A Comprehensive Overview to the Recommender System: Approaches, Algorithms and Challenges
7.1 Section I – Introduction
7.1.1 Working Principles of the Recommender System
7.1.2 Architecture of Recommender System
7.1.3 Ways of Collecting Feedback
7.1.4 Evaluating a Recommender System
7.1.5 Classification of the Recommender System
7.1.6 Contribution of the Chapter
7.1.7 Organization of the Chapter
7.2 Section II – Related Works
7.3 Section III – Challenges of Recommender System
7.3.1 User Privacy
7.3.2 Data Sparsity
7.3.3 Scalability
7.3.4 Freshness
7.3.5 Cold-Start Problem
7.3.6 Profile Injection Attack
7.4 Section IV – Comparative Study of Algorithms in Recommender System
7.5 Conclusion
References
Chapter 8: Collaborative Filtering Techniques: Algorithms and Advances
8.1 Overview of Collaborative Filtering
8.1.1 Approaches of Collaborative Filtering
8.1.2 Challenges
8.1.3 Advances in Collaborative Systems
8.2 Preliminaries
8.3 Baseline Prediction Model
8.4 Model-Based Filtering with Advances
8.4.1 Singular Value Decomposition (SVD)
8.4.2 SVD++
8.4.3 Time-Aware Factor Model
8.4.3.1 Time-Changing Baseline Parameters
8.4.3.2 Time-Changing Factor Model
8.4.4 Comparison of Accuracy
8.5 Memory-Based Model with Advances
8.5.1 Calculation of Similarity Measures
8.5.2 Enrichment of the Neighborhood Model
8.5.2.1 Global Neighborhood Model
8.5.2.2 A Factorized Neighborhood Model
8.5.2.2.1 Factoring Item-Item Relationships
8.5.2.2.2 A User-User Model
8.5.2.3 Temporal Dynamics at Neighbourhood Models
8.6 Conclusion
References
Index
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