Collaborative Filtering Using Data Mining and Analysis

Collaborative Filtering Using Data Mining and Analysis

Vishal Bhatnagar

Copyright: © 2017 |Pages: 309

ISBN13: 9781522504894|ISBN10: 1522504893|EISBN13: 9781522504900

DOI: 10.4018/978-1-5225-0489-4

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Bhatnagar, Vishal, editor. Collaborative Filtering Using Data Mining and Analysis. IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0489-4

APA

Bhatnagar, V. (Ed.). (2017). Collaborative Filtering Using Data Mining and Analysis. IGI Global. https://doi.org/10.4018/978-1-5225-0489-4

Chicago

Bhatnagar, Vishal, ed. Collaborative Filtering Using Data Mining and Analysis. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0489-4

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Internet usage has become a normal and essential aspect of everyday life. Due to the immense amount of information available on the web, it has become obligatory to find ways to sift through and categorize the overload of data while removing redundant material.

Collaborative Filtering Using Data Mining and Analysis evaluates the latest patterns and trending topics in the utilization of data mining tools and filtering practices. Featuring emergent research and optimization techniques in the areas of opinion mining, text mining, and sentiment analysis, as well as their various applications, this book is an essential reference source for researchers and engineers interested in collaborative filtering.

Table of Contents

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Front Materials

Title Page

Copyright Page

Advances in Data Mining and Database Management (ADMDM) Book Series

Editorial Advisory Board and List of Reviewers

Foreword

Preface

Acknowledgment

Chapters

Data Mining Techniques and Analysis: An Overview

Chapter 1

Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System (pages 1-23)

Renuka Mahajan

This chapter revolves around the synthesis of three research areas- data mining, personalization, recommendation systems and adaptive e-Learning systems. It also introduces a comprehensive list of parameters, extricated by reviewing...

Chapter 2

Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value (pages 24-48)

Mamta Mittal, R. K. Sharma, V.P. Singh, Lalit Mohan Goyal

Clustering is one of the data mining techniques that investigates these data resources for hidden patterns. Many clustering algorithms are available in literature. This chapter emphasizes on partitioning based methods and is an...

Chapter 3

Dimensionality Reduction Techniques for Text Mining (pages 49-72)

Neethu Akkarapatty, Anjaly Muralidharan, Nisha S. Raj, Vinod P.

Sentiment analysis is an emerging field, concerned with the analysis and understanding of human emotions from sentences. Sentiment analysis is the process used to determine the attitude/opinion/emotions expressed by a person about a...

Collaborative Filtering: An Introduction

Chapter 4

History and Overview of the Recommender Systems (pages 74-99)

Venkatesan M., Thangadurai K.

This Chapter analyzes the recommender systems, their history and its framework in brief. The current generation of filtering techniques in recommendation methods can be broadly classified into the following five categories....

Chapter 5

A Classification Framework Towards Application of Data Mining in Collaborative Filtering (pages 100-114)

Neeti Sangwan, Naveen Dahiya

Recommendation making is an important part of the information and e-commerce ecosystem. Recommendation represent a powerful method that filter large amount of information to provide relevant choice to end users. To provide...

Chapter 6

Collaborative Filtering Based Data Mining for Large Data (pages 115-127)

Amrit Pal, Manish Kumar

Size of data is increasing, it is creating challenges for its processing and storage. There are cluster based techniques available for storage and processing of this huge amount of data. Map Reduce provides an effective programming...

Chapter 7

Big Data Mining Using Collaborative Filtering (pages 128-138)

Anu Saini

Today every big company, like Google, Flipkart, Yahoo, Amazon etc., is dealing with the Big Data. This big data can be used to predict the recommendation for the user on the basis of their past behavior. Recommendation systems are...

Applications of Data Mining Techniques and Data Analysis in Collaborative Filtering

Chapter 8

Collaborative and Clustering Based Strategy in Big Data (pages 140-158)

Arushi Jain, Vishal Bhatnagar, Pulkit Sharma

There is a proliferation in the amount of data generated and its volume, which is going to persevere for many coming years. Big data clustering is the exercise of taking a set of objects and dividing them into groups in such a way...

Chapter 9

Association Rule Mining in Collaborative Filtering (pages 159-179)

Carson K.-S. Leung, Fan Jiang, Edson M. Dela Cruz, Vijay Sekar Elango

Collaborative filtering uses data mining and analysis to develop a system that helps users make appropriate decisions in real-life applications by removing redundant information and providing valuable to information users. Data...

Chapter 10

A Classification Framework on Opinion Mining for Effective Recommendation Systems (pages 180-194)

Mahima Goyal, Vishal Bhatnagar

With the recent trend of expressing opinions on the social media platforms like Twitter, Blogs, Reviews etc., a large amount of data is available for the analysis in the form of opinion mining. This analysis plays pivotal role in...

Chapter 11

Combining User Co-Ratings and Social Trust for Collaborative Recommendation: A Data Analytics Approach (pages 195-216)

Sheng-Jhe Ke, Wei-Po Lee

Traditional collaborative filtering recommendation methods calculate similarity between users to find the most similar neighbors for a particular user and take into account their opinions to predict item ratings. Though these methods...

Chapter 12

Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey (pages 217-235)

Marenglen Biba, Narasimha Rao Vajjhala, Lediona Nishani

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the...

Chapter 13

Data Stream Mining Using Ensemble Classifier: A Collaborative Approach of Classifiers (pages 236-249)

Snehlata Sewakdas Dongre, Latesh G. Malik

A data stream is giant amount of data which is generated uncontrollably at a rapid rate from many applications like call detail records, log records, sensors applications etc. Data stream mining has grasped the attention of so many...

Chapter 14

Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review (pages 250-269)

Lediona Nishani, Marenglen Biba

People nowadays base their behavior by making choices through word of mouth, media, public opinion, surveys, etc. One of the most prominent techniques of recommender systems is Collaborative filtering (CF), which utilizes the known...

Back Materials

Compilation of References

About the Contributors

Index

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