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Record Nr. |
UNINA9910739476103321 |
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Autore |
Kulkarni Akshay |
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Titolo |
Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques / / by Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (257 pages) |
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Disciplina |
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Soggetti |
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Recommender systems (Information filtering) |
Machine learning |
Neural networks (Computer science) |
Python (Computer program language) |
Artificial intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Chapter 1: Introduction to Recommender Systems -- Chapter 2: Association Rule Mining -- Chapter 3: Content and Knowledge-Based Recommender System -- Chapter 4: Collaborative Filtering using KNN -- Chapter 5: Collaborative Filtering Using Matrix Factorization, SVD and ALS -- Chapter 6: Hybrid Recommender System -- Chapter 7: Clustering Algorithm-Based Recommender System -- Chapter 8: Classification Algorithm-Based Recommender System -- Chapter 9: Deep Learning and NLP Based Recommender System -- Chapter 10: Graph-Based Recommender System. - Chapter 11: Emerging Areas and Techniques in Recommender System. |
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Sommario/riassunto |
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This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today. You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and |
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knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations. By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms. You will: Understand and implement different recommender systems techniques with Python Employ popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filtering Leverage machine learning, NLP, and deep learning for building recommender systems. |
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