PTCCS360 Recommender Systems Syllabus:
PTCCS360 Recommender Systems Syllabus – Anna University Part time Regulation 2023
COURSE OBJECTIVES:
To understand the foundations of the recommender system.
To learn the significance of machine learning and data mining algorithms for Recommender systems
To learn about collaborative filtering
To make students design and implement a recommender system.
To learn collaborative filtering.
UNIT I INTRODUCTION
Introduction and basic taxonomy of recommender systems – Traditional and non-personalized Recommender Systems – Overview of data mining methods for recommender systems- similarity measures- Dimensionality reduction – Singular Value Decomposition (SVD)
Suggested Activities:
Practical learning – Implement Data similarity measures.
External Learning – Singular Value Decomposition (SVD) applications
Suggested Evaluation Methods:
Quiz on Recommender systems.
Quiz of python tools available for implementing Recommender systems
UNIT II CONTENT-BASED RECOMMENDATION SYSTEMS
High-level architecture of content-based systems – Item profiles, Representing item profiles, Methods for learning user profiles, Similarity-based retrieval, and Classification algorithms.
Suggested Activities:
Assignment on content-based recommendation systems
Assignment of learning user profiles
Suggested Evaluation Methods:
Quiz on similarity-based retrieval.
Quiz of content-based filtering
UNIT III COLLABORATIVE FILTERING
A systematic approach, Nearest-neighbor collaborative filtering (CF), user-based and item-based CF, components of neighborhood methods (rating normalization, similarity weight computation, and neighborhood selection
Suggested Activities:
● Practical learning – Implement collaborative filtering concepts
● Assignment of security aspects of recommender systems
Suggested Evaluation Methods:
● Quiz on collaborative filtering
● Seminar on security measures of recommender systems
UNIT IV ATTACK-RESISTANT RECOMMENDER SYSTEMS
Introduction – Types of Attacks – Detecting attacks on recommender systems – Individual attack – Group attack – Strategies for robust recommender design – Robust recommendation algorithms.
Suggested Activities:
● Group Discussion on attacks and their mitigation
● Study of the impact of group attacks
● External Learning – Use of CAPTCHAs
Suggested Evaluation Methods:
● Quiz on attacks on recommender systems
● Seminar on preventing attacks using the CAPTCHAs
UNIT V EVALUATING RECOMMENDER SYSTEMS
Evaluating Paradigms – User Studies – Online and Offline evaluation – Goals of evaluation design – Design Issues – Accuracy metrics – Limitations of Evaluation measures
Suggested Activities:
● Group Discussion on goals of evaluation design
● Study of accuracy metrics
Suggested Evaluation Methods:
● Quiz on evaluation design
● Problems on accuracy measures 30 PERIODS
PRACTICAL EXERCISES 30 PERIODS
1. Implement Data similarity measures using Python
2. Implement dimension reduction techniques for recommender systems
3. Implement user profile learning
4. Implement content-based recommendation systems
5. Implement collaborative filter techniques
6. Create an attack for tampering with recommender systems
7. Implement accuracy metrics like Receiver Operated Characteristic curves
TOTAL: 60 PERIODS
COURSE OUTCOMES:
On completion of the course, the students will be able to:
CO1:Understand the basic concepts of recommender systems.
CO2:Implement machine-learning and data-mining algorithms in recommender systems data sets.
CO3:Implementation of Collaborative Filtering in carrying out performance evaluation of recommender systems based on various metrics.
CO4:Design and implement a simple recommender system.
CO5:Learn about advanced topics of recommender systems.
CO6:Learn about advanced topics of recommender systems applications
TEXTBOOKS:
1. Charu C. Aggarwal, Recommender Systems: The Textbook, Springer, 2016.
2. Dietmar Jannach , Markus Zanker , Alexander Felfernig and Gerhard Friedrich , Recommender Systems: An Introduction, Cambridge University Press (2011), 1st ed.
3. Francesco Ricci , Lior Rokach , Bracha Shapira , Recommender Sytems Handbook, 1st ed, Springer (2011),
4. Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Mining of massive datasets, 3rd edition, Cambridge University Press, 2020.