Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques

by Akshay Kulkarni

Chapter 1: Introduction to Recommender SystemsChapter Introduction of recommender systems, along with a high-level overview of how recommender systems work, what are the different existing types, and how to leverage basic and advanced machine learning techniques to build these systems.No of 25Sub -
1. Intro to recommender system 2. How it works3. Types and how they worka. Association rule miningb. Content basedc. Collaborative filtering d. Hybrid systemse. ML Clustering basedf. ML Classification basedg. Deep learning and NLP basedh. Graph based
Chapter 2: Association Rule MiningChapter Building one of the simplest recommender systems from scratch, using association rule mining; also called market basket analysis.No of 20Sub - Topics 1 APRIORI2 FP GROWTH3 Advantages and Disadvantages
Chapter 3: Content and Knowledge-Based Recommender SystemChapter Building the content and knowledge-based recommender system from scratch using both product content and demographicsNo of 25Sub - Topics 1 TF-IDF2 BOW3 Transformer based4 Advantages and disadvantages

Chapter 4: Collaborative Filtering using KNNChapter Building the collaborative filtering using KNN from scratch, both item-item and user-user basedNo of 25Sub - 1 KNN - item based2 KNN - user based3 Advantages and disadvantages

Chapter 5: Collaborative Filtering Using Matrix Factorization, SVD and ALS.Chapter Building the collaborative filtering using SVM from scratch, both item-item and user-user basedNo of 25Sub - 1 Latent factors2 SVD3 ALS4 Advantages and disadvantages

Chapter 6: Hybrid Recommender SystemChapter Building the hybrid recommender system (Using both content and collaborative methods) which is widely used in the industryNo of 25Sub - 1 a different weight given to the recommenders of each technique used to favor some of them.2 a single set of recommenders, without favorites.3 suggestions from one system are used as input for the next, and so on until the last one.4 Choosing a random method5 Advantages and disadvantages

Chapter 7: Clustering Algorithm-Based Recommender SystemChapter Building the clustering model for recommender systems.No of 25Sub - 1 K means clustering2 Hierarchal clustering 3 Advantages and disadvantages

Chapter 8: Classification Algorithm-Based Recommender SystemChapter Building the classification model for recommender systems.No of 25Sub - 1 Buying propensity model2 Logistic regression3 Random forest4 SVM5 Advantages and disadvantages

Chapter 9: Deep Learning and NLP Based Recommender SystemChapter Building state of art recommender system using advanced topics like Deep learning along with NLP (Natural Language processing).No of 25Sub - 1 Word embedding's2 Deep neural networks3 Advantages and disadvantages

264 pages, Paperback

Published December 6, 2022