CCS360 RECOMMENDER SYSTEMS

 CCS360 RECOMMENDER SYSTEMS

 UNIT I INTRODUCTION     

Traditional and non-personalized Recommender Systems 

Overview of data mining methods for recommender systems***

(SVD)**

 UNIT II CONTENT-BASED RECOMMENDATION SYSTEMS   

 High-level architecture of content-based systems 

Methods for learning user profiles, Similarity-based retrieval

 Classification algorithms.  

 UNIT III COLLABORATIVE FILTERING   

neighbor collaborative filtering (CF), user-based and item-based CF

rating normalization, similarity weight computation , neighborhood selection .***

UNIT IV ATTACK-RESISTANT RECOMMENDER SYSTEMS

Types of Attacks - Detecting attacks on recommender systems ***

Individual attack – Group attACK

Strategies for robust recommender design - Robust recommendation algorithms.**

  UNIT V EVALUATING RECOMMENDER SYSTEMS  

Online and Offline evaluation ,Goals of evaluation design 

Design Issues, Limitations of Evaluation measures.***