Recommender Systems detailed syllabus for Computer Science & Business Systems (CS&BS) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the CS&BS 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 Science & Business Systems 6th Sem scheme and its subjects, do visit CS&BS 6th Sem 2021 regulation scheme. For Professional Elective-VI scheme and its subjects refer to CS&BS Professional Elective-VI 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 Science & Business Systems 6th Sem, visit CS&BS 6th Sem subject syllabuses for 2021 regulation.
For all Computer Science & Business Systems results, visit Anna University CS&BS all semester results direct link.