LEADER 04311nam 22005775 450 001 9910739476103321 005 20230224131631.0 010 $a1-4842-8954-4 024 7 $a10.1007/978-1-4842-8954-9 035 $a(MiAaPQ)EBC7144566 035 $a(Au-PeEL)EBL7144566 035 $a(CKB)25456757900041 035 $a(OCoLC)1351749786 035 $a(OCoLC-P)1351749786 035 $a(DE-He213)978-1-4842-8954-9 035 $a(CaSebORM)9781484289549 035 $a(PPN)26635680X 035 $a(EXLCZ)9925456757900041 100 $a20221121d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied Recommender Systems with Python $eBuild Recommender Systems with Deep Learning, NLP and Graph-Based Techniques /$fby Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan 205 $a1st ed. 2023. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2023. 215 $a1 online resource (257 pages) 300 $aIncludes index. 311 08$aPrint version: Kulkarni, Akshay Applied Recommender Systems with Python Berkeley, CA : Apress L. P.,c2022 9781484289532 327 $aChapter 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. 330 $aThis 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 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. 606 $aRecommender systems (Information filtering) 606 $aMachine learning 606 $aNeural networks (Computer science) 606 $aPython (Computer program language) 606 $aArtificial intelligence 615 0$aRecommender systems (Information filtering) 615 0$aMachine learning. 615 0$aNeural networks (Computer science) 615 0$aPython (Computer program language) 615 0$aArtificial intelligence. 676 $a006.3 700 $aKulkarni$b Akshay$01376507 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910739476103321 996 $aApplied Recommender Systems with Python$93553231 997 $aUNINA