Week 1:
Introduction
Business value of Recommender System
A conceptual framework for understanding recommender system
Types of recommender system
Week 2: Data for recommendation: Explicit Vs Implicit data collection
Scales of measurement
Statistical and machine learning foundations for recommender system
Data preprocessing
Week 3: Introduction to collaborative filtering
Collaborative filtering approaches: Memory based and model based
Memory based collaborative filtering foundations: Distance and similarity measures
User based collaborative filtering; Item based collaborative filtering
Week 4: Model based collaborative filtering foundations: matrix factorization, UV decomposition, Singular value decomposition Model based collaborative filtering techniques: SVD, SVD++ etc
Week 5: Content based recommender system foundations
Examples with text data
Feature engineering: Feature extraction, feature selection, dimensionality reduction
Week 6: Content based recommender system examples with few supervised machine learning techniques
Week 7: Evaluation of recommender systems: Online and offline evaluation, metrics such as RMSE, AME, Good Item MAE, Good predicted item MAE, Precision, Recall, F1 Measure, NDCG, Average Reciprocal Rank, Top@N Measure.
Week 8: Overview of other types of recommender systems such as trust based, social network based, and context aware systems
DOWNLOAD APP
FOLLOW US