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B.E. (Artificial Intelligence and Data Science) Recommendation Systems Syllabus - Mumbai University

This is the Fourth Year BE AI and DS syllabus under CBCS REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has published no NEP 2020 syllabus for Semesters V to VIII of any engineering branch, so this is the scheme you are examined on — exam form 1T01817 and 1T01818. The first and second years of the degree are on NEP 2020.

Recommendation Systems.pdf
Semester 8 · Fourth Year BE AI and DS · 3 credits · CBCS REV-2019 'C' Scheme

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Syllabus for Recommendation Systems

Semester 8 · Fourth Year BE AI and DS · 3 credits · CBCS REV-2019 'C' Scheme

Module 1.0 06 hours

  • 1.0 Introduction to Recommendation System
  • 1.1 1.2 History of recommendation system, Eliciting Ratings and other Feedback Contributions, Implicit and Implicit Ratings, Recommender system functions. Linear Algebra notation: Matrix addition, Multiplication, transposition, and inverses; covariance matrices, Understanding ratings, Applications of recommendation systems, Issues with recommender system.
  • 2.0 Collaborative Filtering
  • 2.1 Architecture of Collaborative Filtering, User-based nearest neighbour recommendation, Item-based nearest neighbour recommendation, Model based and pre-processing based approaches, Clustering for recommendation system, Attacks on collaborative recommender systems, Advantages and drawbacks of Collaborative Filtering.
  • 3.0 Content-based recommendation
  • 3.1 3.2 Architecture of content-based systems, Content representation and content similarity, Item profiles, Discovering features of documents, Obtaining item features from tags, Representing item profiles, Methods for learning user profiles, Similarity based retrieval, The Role of User Generated Content in the Recommendation Process. Bayes classifier for recommendation, Regression based recommendation system. Advantages and drawbacks of content-based filtering
  • 4.0 Knowledge based recommendation
  • 4.1 Knowledge representation and reasoning, Constraint based recommenders, Case based recommenders, Persistent Personalization in Knowledge-Based Systems, Conversational Recommendation. Search based recommendation, Navigation-based recommendation.
  • 5.0 Ensembled- Based and Hybrid Recommendation System
  • 5.1 Opportunities for hybridization, Monolithic hybridization design: Feature combination, Feature augmentation, Parallelized hybridization design: Weighted, Switching, Mixed, Pipelined hybridization design: Cascade Meta-level, Limitations of hybridization strategies.
  • 6.0 Evaluating Recommendation System
  • 6.1 6.2 Characteristics and properties of evaluation research, Evaluation design goals- Accuracy, Coverage, Confidence and Trust, Novelty, Serendipity, Diversity, Robustness, Stability and Scalability. Comparison between evaluation design of classification model and recommendation system, Error metrics, Decision-Support metrics, User-Centred metrics. Comparative analysis between different types of recommendation systems.

Text Books

  • 1 Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: an introduction. Cambridge University Press.
  • 2 Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to Recommender Systems Handbook. Springer, Boston, MA.

References

  • 1 Aggarwal, C. C. (2016). Recommender systems (Vol. 1). Cham: Springer International Publishing.

Online References

  • 1 http://www.iem.iitkgp.ac.in/eco/Recommender_Systems/
  • 2 https://www.coursera.org/specializations/recommender-systems
  • 3 https://www.udemy.com/course/recommender-systems/
  • 4 https://www.analyticsvidhya.com/blog/2021/08/developing-a-course-recommender-system-using-python/

Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Data Science), item 6.12 (N), under CBCS REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording, module numbering and hours are as printed in that syllabus.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

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