PTCCS360 Syllabus - Recommender Systems - 2023 Regulation Anna University

PTCCS360 Syllabus - Recommender Systems - 2023 Regulation Anna University

PTCCS360	RECOMMENDER SYSTEMS	L T P C

2 0 2 3

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	6

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	6

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	6

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	6

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	6

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

TEXT BOOKS:

1. Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2016.
2. Francois Chollet, “Deep Learning with Python”, Second Edition, Manning Publications, 2021.

REFERENCES:

1. Aurélien Géron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Oreilly,2018.
2. Josh Patterson, Adam Gibson, “Deep Learning: A Practitioner’s Approach”, O’Reilly Media,2017.
3. Charu C. Aggarwal, “Neural Networks and Deep Learning: A Textbook”, SpringerInternational Publishing, 1st Edition, 2018.
4. Learn Keras for Deep Neural Networks, Jojo Moolayil, Apress,2018
5. Deep Learning Projects Using TensorFlow 2, Vinita Silaparasetty, Apress, 2020
6. Deep Learning with Python, FRANÇOIS CHOLLET, MANNING SHELTER ISLAND,2017.
7. S Rajasekaran, G A Vijayalakshmi Pai, “Neural Networks, FuzzyLogic and GeneticAlgorithm, Synthesis and Applications”, PHI Learning, 2017.
8. Pro Deep Learning with TensorFlow, Santanu Pattanayak, Apress,2017
9. James A Freeman, David M S Kapura, “Neural Networks Algorithms, Applications, andProgramming Techniques”, Addison Wesley, 2003.