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

Chapter 1: Introduction to Recommender SystemsChapter Goal: 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 pages: 25Sub - Topics: 

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 Goal: Building one of the simplest recommender systems from scratch, using association rule mining; also called market basket analysis.No of pages: 20Sub - Topics1APRIORI2FP GROWTH3Advantages and Disadvantages

Chapter 3: Content and Knowledge-Based Recommender SystemChapter Goal: Building the content and knowledge-based recommender system from scratch using both product content and demographicsNo of pages: 25Sub - Topics1TF-IDF2BOW3Transformer based4Advantages and disadvantages

Chapter 4: Collaborative Filtering using KNNChapter Goal: Building the collaborative filtering using KNN from scratch, both item-item and user-user basedNo of pages: 25Sub - Topics: 1KNN - item based2KNN - user based3Advantages and disadvantages

Chapter 5: Collaborative Filtering Using Matrix Factorization, SVD and ALS.Chapter Goal: Building the collaborative filtering using SVM from scratch, both item-item and user-user basedNo of pages: 25Sub - Topics: 1Latent factors2SVD3ALS4Advantages and disadvantages

Chapter 6: Hybrid Recommender SystemChapter Goal: Building the hybrid recommender system (Using both content and collaborative methods) which is widely used in the industryNo of pages: 25Sub - Topics: 1Weighted: a different weight given to the recommenders of each technique used to favor some of them.2Mixed: a single set of recommenders, without favorites.3Augmented: suggestions from one system are used as input for the next, and so on until the last one.4Switching: Choosing a random method5Advantages and disadvantages

Chapter 7: Clustering Algorithm-Based Recommender SystemChapter Goal: Building the clustering model for recommender systems.No of pages: 25Sub - Topics: 1K means clustering2Hierarchal clustering 3Advantages and disadvantages

Chapter 8: Classification Algorithm-Based Recommender SystemChapter Goal: Building the classification model for recommender systems.No of pages: 25Sub - Topics: 1Buying propensity model2Logistic regression3Random forest4SVM5Advantages and disadvantages

Chapter 9: Deep Learning and NLP Based Recommender SystemChapter Goal: Building state of art recommender system using advanced topics like Deep learning along with NLP (Natural Language processing).No of pages: 25Sub - Topics: 1Word embedding's2Deep neural networks3Advantages and disadvantages