Predicting Movie Ratings And Recommender Systems

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Predicting movie ratings and recommender systems

Author : Arkadiusz Paterek
Publisher : Arkadiusz Paterek
ISBN 13 :
Total Pages : 196 pages
Book Rating : 4./5 ( download)

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Book Synopsis Predicting movie ratings and recommender systems by : Arkadiusz Paterek

Download or read book Predicting movie ratings and recommender systems written by Arkadiusz Paterek and published by Arkadiusz Paterek. This book was released on 2012-06-19 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: A 195-page monograph by a top-1% Netflix Prize contestant. Learn about the famous machine learning competition. Improve your machine learning skills. Learn how to build recommender systems. What's inside:introduction to predictive modeling,a comprehensive summary of the Netflix Prize, the most known machine learning competition, with a $1M prize,detailed description of a top-50 Netflix Prize solution predicting movie ratings,summary of the most important methods published - RMSE's from different papers listed and grouped in one place,detailed analysis of matrix factorizations / regularized SVD,how to interpret the factorization results - new, most informative movie genres,how to adapt the algorithms developed for the Netflix Prize to calculate good quality personalized recommendations,dealing with the cold-start: simple content-based augmentation,description of two rating-based recommender systems,commentary on everything: novel and unique insights, know-how from over 9 years of practicing and analysing predictive modeling.

Approaching (Almost) Any Machine Learning Problem

Author : Abhishek Thakur
Publisher : Abhishek Thakur
ISBN 13 : 8269211508
Total Pages : 300 pages
Book Rating : 4.2/5 (692 download)

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Book Synopsis Approaching (Almost) Any Machine Learning Problem by : Abhishek Thakur

Download or read book Approaching (Almost) Any Machine Learning Problem written by Abhishek Thakur and published by Abhishek Thakur. This book was released on 2020-07-04 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option. This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along. Table of contents: - Setting up your working environment - Supervised vs unsupervised learning - Cross-validation - Evaluation metrics - Arranging machine learning projects - Approaching categorical variables - Feature engineering - Feature selection - Hyperparameter optimization - Approaching image classification & segmentation - Approaching text classification/regression - Approaching ensembling and stacking - Approaching reproducible code & model serving There are no sub-headings. Important terms are written in bold. I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, visit this link: https://bit.ly/aamlquestions And Subscribe to my youtube channel: https://bit.ly/abhitubesub

A Study of the Drivers of Movie Rating Recommendations

Author : Dhruv Sharma
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Book Synopsis A Study of the Drivers of Movie Rating Recommendations by : Dhruv Sharma

Download or read book A Study of the Drivers of Movie Rating Recommendations written by Dhruv Sharma and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, online retailers have increasingly relied on state of the art algorithms to help predict user satisfaction and recommend movies to consumers. The major limitation of advanced state of the art computational algorithms such as SVD and latent factor models using implicit feedback is that these conceal the marginal effects of the most significant independent variables. This paper uses traditional linear regression to develop a recommender system which is as good as more advanced computational algorithms. Results reveal that the most important variables in predicting movie recommendations are ratings of user with extreme differences on other movies, age specific (conditioned) effects of movie ratings per movie and user average ratings conditioned on genres. These 3 elements allow regression models to match black box and matrix algorithms in recommender movie rating performance. This furthers the science behind recommender and also discusses a shock expectation shock minimization theory for enhancing movie watching experiences. Priming the users with appropriate expectations or frames for watching a movie should enhance user satisfaction. This supports query theory which states preferences are not recalled but dynamically built and can be influenced.

Mahout in Action

Author : Sean Owen
Publisher : Simon and Schuster
ISBN 13 : 1638355371
Total Pages : 616 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Mahout in Action by : Sean Owen

Download or read book Mahout in Action written by Sean Owen and published by Simon and Schuster. This book was released on 2011-10-04 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Mahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Includes a free audio- and video-enhanced ebook. About the Technology A computer system that learns and adapts as it collects data can be really powerful. Mahout, Apache's open source machine learning project, captures the core algorithms of recommendation systems, classification, and clustering in ready-to-use, scalable libraries. With Mahout, you can immediately apply to your own projects the machine learning techniques that drive Amazon, Netflix, and others. About this Book This book covers machine learning using Apache Mahout. Based on experience with real-world applications, it introduces practical use cases and illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability and how to apply these techniques against large data sets using the Apache Hadoop framework. This book is written for developers familiar with Java -- no prior experience with Mahout is assumed. Owners of a Manning pBook purchased anywhere in the world can download a free eBook from manning.com at any time. They can do so multiple times and in any or all formats available (PDF, ePub or Kindle). To do so, customers must register their printed copy on Manning's site by creating a user account and then following instructions printed on the pBook registration insert at the front of the book. What's Inside Use group data to make individual recommendations Find logical clusters within your data Filter and refine with on-the-fly classification Free audio and video extras Table of Contents Meet Apache Mahout PART 1 RECOMMENDATIONS Introducing recommenders Representing recommender data Making recommendations Taking recommenders to production Distributing recommendation computations PART 2 CLUSTERING Introduction to clustering Representing data Clustering algorithms in Mahout Evaluating and improving clustering quality Taking clustering to production Real-world applications of clustering PART 3 CLASSIFICATION Introduction to classification Training a classifier Evaluating and tuning a classifier Deploying a classifier Case study: Shop It To Me

Predicting Film Ratings Using Collaborative Filtering and Deep Learning

Author : Annie Jiali Zhang
Publisher :
ISBN 13 :
Total Pages : 40 pages
Book Rating : 4.:/5 (122 download)

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Book Synopsis Predicting Film Ratings Using Collaborative Filtering and Deep Learning by : Annie Jiali Zhang

Download or read book Predicting Film Ratings Using Collaborative Filtering and Deep Learning written by Annie Jiali Zhang and published by . This book was released on 2020 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recommender systems help people make decisions. They are particularly useful for product recommendation, a setting where favorable products are suggested to a customer. For streaming services, products may consist of television shows and films. Research has shown that various approaches can be used to build successful recommender systems. In this paper, we survey linear models, content-based methods, collaborative filtering, and deep learning. We have an in-depth discussion of collaborative filtering and deep learning systems and present the strengths and weaknesses of each approach. We use the benchmark MovieLens dataset to demonstrate the performance of two models for predicting film rating. Our results reveal that a neural network model outperforms an item-based collaborative filtering model for product recommendation.

Grokking Machine Learning

Author : Luis Serrano
Publisher : Simon and Schuster
ISBN 13 : 1617295914
Total Pages : 510 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis Grokking Machine Learning by : Luis Serrano

Download or read book Grokking Machine Learning written by Luis Serrano and published by Simon and Schuster. This book was released on 2021-12-14 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.

Recommender Systems

Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319296590
Total Pages : 518 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Recommender Systems by : Charu C. Aggarwal

Download or read book Recommender Systems written by Charu C. Aggarwal and published by Springer. This book was released on 2016-03-28 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. 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. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.

Recommender Systems Handbook

Author : Francesco Ricci
Publisher : Springer
ISBN 13 : 148997637X
Total Pages : 1008 pages
Book Rating : 4.4/5 (899 download)

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Book Synopsis Recommender Systems Handbook by : Francesco Ricci

Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer. This book was released on 2015-11-17 with total page 1008 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.

Recommender System with Machine Learning and Artificial Intelligence

Author : Sachi Nandan Mohanty
Publisher : John Wiley & Sons
ISBN 13 : 1119711576
Total Pages : 448 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Recommender System with Machine Learning and Artificial Intelligence by : Sachi Nandan Mohanty

Download or read book Recommender System with Machine Learning and Artificial Intelligence written by Sachi Nandan Mohanty and published by John Wiley & Sons. This book was released on 2020-07-08 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Collaborative Filtering Recommender Systems

Author : Michael D. Ekstrand
Publisher : Now Publishers Inc
ISBN 13 : 1601984421
Total Pages : 104 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis Collaborative Filtering Recommender Systems by : Michael D. Ekstrand

Download or read book Collaborative Filtering Recommender Systems written by Michael D. Ekstrand and published by Now Publishers Inc. This book was released on 2011 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collaborative Filtering Recommender Systems discusses a wide variety of the recommender choices available and their implications, providing both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.

Encyclopedia of Machine Learning

Author : Claude Sammut
Publisher : Springer Science & Business Media
ISBN 13 : 0387307680
Total Pages : 1061 pages
Book Rating : 4.3/5 (873 download)

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Book Synopsis Encyclopedia of Machine Learning by : Claude Sammut

Download or read book Encyclopedia of Machine Learning written by Claude Sammut and published by Springer Science & Business Media. This book was released on 2011-03-28 with total page 1061 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.

Simultaneous Regression and Clustering to Predict Movie Ratings

Author : Matthew Rodriguez
Publisher :
ISBN 13 :
Total Pages : 50 pages
Book Rating : 4.:/5 (547 download)

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Book Synopsis Simultaneous Regression and Clustering to Predict Movie Ratings by : Matthew Rodriguez

Download or read book Simultaneous Regression and Clustering to Predict Movie Ratings written by Matthew Rodriguez and published by . This book was released on 2010 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: A recommender system uses information from a user's past behavior to present items of interest to him. A fundamental problem in recommender systems is approximating a full user-item matrix where most of the entries are missing. The rows of the matrix represent the users and the columns represent the items. The entries indicate the plausibility that the user will enjoy the item. In this thesis the items are movies and the entries ratings. In this thesis I compare three statistical models that how a user will rate a movie. The first two are Bernoulli models that predict whether a rating is greater than three out of five. The first Bernoulli model uses logistic regression. The second Bernoulli model is a latent factor model. The third model extends the latent factor model to use a five class multinomial. A five class multinomial is chosen to predict a rating on a scale of one to five. The results show that latent factor model that uses a Bernoulli distribution has a better accuracy than a model trained by logistic regression. The latent factor model is extended to use a multinomial. The accuracy of variants of the multinomial model are evaluated. A technique to initialize the multinomial model is shown to improve the accuracy. However the accuracy is lower than other models used in the Netflix competition. The Bernoulli and multinomial latent factor models are compared against each other. The Bernoulli model is more accurate.

Recommender Systems

Author : Monideepa Roy
Publisher : CRC Press
ISBN 13 : 100088628X
Total Pages : 261 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Recommender Systems by : Monideepa Roy

Download or read book Recommender Systems written by Monideepa Roy and published by CRC Press. This book was released on 2023-06-19 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book: Identifies and describes recommender systems for practical uses Describes how to design, train, and evaluate a recommendation algorithm Explains migration from a recommendation model to a live system with users Describes utilization of the data collected from a recommender system to understand the user preferences Addresses the security aspects and ways to deal with possible attacks to build a robust system This book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.

2021 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

Author : IEEE Staff
Publisher :
ISBN 13 : 9781665447867
Total Pages : pages
Book Rating : 4.4/5 (478 download)

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Book Synopsis 2021 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) by : IEEE Staff

Download or read book 2021 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) written by IEEE Staff and published by . This book was released on 2021-06-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic Control and Intelligent Systems System Identification and Modeling Signal Processing Instrumentation and Automation Technological Advancements Power Systems Engineering Communication Engineering Electronics Engineering Computer Engineering

Recommender Systems and the Social Web

Author : Fatih Gedikli
Publisher : Springer Science & Business Media
ISBN 13 : 3658019484
Total Pages : 118 pages
Book Rating : 4.6/5 (58 download)

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Book Synopsis Recommender Systems and the Social Web by : Fatih Gedikli

Download or read book Recommender Systems and the Social Web written by Fatih Gedikli and published by Springer Science & Business Media. This book was released on 2013-03-29 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is an increasing demand for recommender systems due to the information overload users are facing on the Web. The goal of a recommender system is to provide personalized recommendations of products or services to users. With the advent of the Social Web, user-generated content has enriched the social dimension of the Web. As user-provided content data also tells us something about the user, one can learn the user’s individual preferences from the Social Web. This opens up completely new opportunities and challenges for recommender systems research. Fatih Gedikli deals with the question of how user-provided tagging data can be used to build better recommender systems. A tag recommender algorithm is proposed which recommends tags for users to annotate their favorite online resources. The author also proposes algorithms which exploit the user-provided tagging data and produce more accurate recommendations. On the basis of this idea, he shows how tags can be used to explain to the user the automatically generated recommendations in a clear and intuitively understandable form. With his book, Fatih Gedikli gives us an outlook on the next generation of recommendation systems in the Social Web sphere.

Recommender Systems Handbook

Author : Francesco Ricci
Publisher : Springer Science & Business Media
ISBN 13 : 0387858202
Total Pages : 848 pages
Book Rating : 4.3/5 (878 download)

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Book Synopsis Recommender Systems Handbook by : Francesco Ricci

Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer Science & Business Media. This book was released on 2010-10-21 with total page 848 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. Recommender Systems Handbook, an edited volume, 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. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.

International Conference on Communication, Computing and Electronics Systems

Author : V. Bindhu
Publisher : Springer Nature
ISBN 13 : 9813349093
Total Pages : 821 pages
Book Rating : 4.8/5 (133 download)

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Book Synopsis International Conference on Communication, Computing and Electronics Systems by : V. Bindhu

Download or read book International Conference on Communication, Computing and Electronics Systems written by V. Bindhu and published by Springer Nature. This book was released on 2021-03-25 with total page 821 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes high-quality papers presented at the International Conference on Communication, Computing and Electronics Systems 2020, held at the PPG Institute of Technology, Coimbatore, India, on 21–22 October 2020. The book covers topics such as automation, VLSI, embedded systems, integrated device technology, satellite communication, optical communication, RF communication, microwave engineering, artificial intelligence, deep learning, pattern recognition, Internet of Things, precision models, bioinformatics, and healthcare informatics.