CP4285: Modern Recommendation Systems examines the algorithms, data, and design trade-offs behind contemporary recommender systems. The course connects classical recommendation methods with modern neural approaches, and emphasizes practical implementation, careful evaluation, and responsible deployment in real-world settings.
CP4285 covers classical methods, neural architectures, ranking and retrieval pipelines, sequential models, graph-based recommendation, online learning, and emerging LLM-based approaches. Ethical issues are interwoven throughout, including bias, fairness, privacy, exposure, transparency, and stakeholder impact. Students apply these ideas in a hands-on group project requiring problem formulation, dataset work, baselines, model design, evaluation, critique, and either a final presentation or report.
The lecture slides for this course are openly shared with the public. If you use these materials for learning, teaching, or another purpose, Min would be delighted to hear about your experience—please share it by email.
The course is hosted by WING.NUS, the Web IR / NLP research group at NUS led by Min.
Prerequisites: CS2109S (Introduction to AI and Machine Learning) or equivalent, and completion of at least 120 units. Students without CS2109S but with equivalent background may seek approval from Min.
Workload: (2-0-0-3-5) — 2 hours lecture, 3 hours projects and assignments, 5 hours preparatory and other work; approximately 10 hours per week.
By the end of the course, students should be able to:
| CLO | Outcome |
|---|---|
| CLO 1 | Explain and compare classical recommendation methods, including collaborative filtering, content-based filtering, and hybrid approaches. |
| CLO 2 | Implement matrix factorization and neural recommendation models using modern deep learning frameworks. |
| CLO 3 | Design and execute rigorous offline evaluation protocols using appropriate ranking metrics. |
| CLO 4 | Analyse the cold-start problem and propose strategies to address it. |
| CLO 5 | Critique recommender systems from fairness, privacy, transparency, and stakeholder impact perspectives. |
| CLO 6 | Explain advanced recommendation architectures including sequential, graph-based, multi-objective, and LLM-enhanced systems. |
| CLO 7 | Design and justify a complete recommendation system pipeline from problem formulation through evaluation and ethical analysis. |
| CLO 8 | Communicate and defend recommendation-system designs and results to a technical audience. |