Recommender Systems detailed syllabus for Computer & Communication Engineering (CCE) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the CCE students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Computer & Communication Engineering 5th Sem scheme and its subjects, do visit CCE 5th Sem 2021 regulation scheme. For Professional Elective-I scheme and its subjects refer to CCE Professional Elective-I syllabus scheme. The detailed syllabus of recommender systems is as follows.
Course Objectives:
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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
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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
Practical Exercises
- Implement Data similarity measures using Python
- Implement dimension reduction techniques for recommender systems
- Implement user profile learning
- Implement content-based recommendation systems
- Implement collaborative filter techniques
- Create an attack for tampering with recommender systems
- Implement accuracy metrics like Receiver Operated Characteristic curves
Course Outcomes:
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Text Books:
- Charu C. Aggarwal, Recommender Systems: The Textbook, Springer, 2016.
- Dietmar Jannach , Markus Zanker , Alexander Felfernig and Gerhard Friedrich , Recommender Systems: An Introduction, Cambridge University Press (2011), 1st ed.
- Francesco Ricci , Lior Rokach , Bracha Shapira , Recommender Sytems Handbook, 1st ed, Springer (2011),
- Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Mining of massive datasets, 3rd edition, Cambridge University Press, 2020.
For detailed syllabus of all the other subjects of Computer & Communication Engineering 5th Sem, visit CCE 5th Sem subject syllabuses for 2021 regulation.
For all Computer & Communication Engineering results, visit Anna University CCE all semester results direct link.