Assignments
Changelog
- 10 Aug 2026 — Clarified quiz AI restrictions and the alternative final project deliverables.
- 10 Aug 2026 — Default assignment deadlines are Mon, 23:59 SGT.
- 10 Aug 2026 — Project teams are 2–4 students, with Min able to adjust membership to support diversity.
Coursework is organized around steady participation, written critique, project work, and short checks on core concepts. The final exam assesses individual understanding across the semester.
Coursework Components
| Component | Weightage | Description |
|---|---|---|
| Class Participation | 10% | Active contribution to classes, project critique, peer feedback, and in-class discussion. |
| Essays | 20% | Short written analyses of recommender-system methods, evaluation choices, and social implications. |
| Project / Group Project | 30% | Team project covering problem formulation, dataset preparation, baselines, model design, evaluation, error analysis, ethical analysis, and either a final presentation or a report. |
| Quizzes / Tests | 10% | Short in-class checks on foundational concepts, metrics, and model design choices. AI tools are not permitted during quizzes. |
| Final Exam | 30% | Individual final examination covering the full course. |
Submission Notes
- Unless stated otherwise, assignment deadlines are Mon, 23:59 SGT.
- Submission channels, file formats, and deadline exceptions will be announced by Min.
- Group submissions should clearly state each member’s contribution.
- Written work should include enough methodological detail for Min to assess assumptions, implementation choices, evaluation design, and ethical reasoning.
- Lecture slides are the authoritative course record. Canvas and this website are supporting references; if any detail appears inconsistent, follow the slides and check with Min.
Essays
There are two individual take-home essays. Each essay is worth 10%: 8% for the written analysis and 2% for randomized peer review. Essay 1 is due Mon, 24 Aug 2026, 23:59 SGT; Essay 2 is due Mon, 5 Oct 2026, 23:59 SGT.
AI tools may be used as a resource, but an AI declaration is mandatory and the analysis must remain your own work.
Group Project
A key part of mastering any skill is practicing it beyond the formal algorithmic basis. Projects form an integral part of the assessment (30% of total marks). Teams have 2 to 4 members. Students may form teams themselves, but Min has final decision on group formation and may override student-formed groups to support diversity. No peer review is required. Min will correspond with groups and check in on a regular basis. It is your responsibility to ensure that your group meets with Min, not Min’s responsibility to chase you.
Min will propose a set of suitable recommendation system datasets for student groups to work with. Details will be released in Canvas Files.
Note that performance on macroscopic metrics alone is not the critical factor in your grade. We primarily evaluate with respect to the interesting and well-motivated ideas your team employs to solve the task, the quality of your evaluation, and your ethical analysis.
Project Milestones
| Milestone | Week | Description | Weight |
|---|---|---|---|
| Project Mini-team Declaration | Week 03 — Mon, 24 Aug 2026, 23:59 SGT | Submit a self-formed team (2–4 students) or indicate that you need instructor placement; Min may adjust groups to support diversity. | — |
| Project Design Critique | Week 7 | In-class workshop. Each team presents their application domain, dataset, user problem, recommendation objective, baselines, evaluation plan, and ethical risks for peer critique. | Part of Participation |
| Final Project Presentation | Week 13 — Wed, 11 Nov 2026 | Teams selected to present at 29th STePS present their implemented recommender, evaluation results, error analysis, ethical analysis, and future improvements. They do not submit a project report. | Part of Project (30%) |
| Final Project Report | Wed, 11 Nov 2026, 23:59 SGT | Teams not presenting at 29th STePS submit a written report covering problem formulation, dataset preparation, baselines, model design, evaluation, error analysis, and ethical analysis. They do not give a final project presentation. | Part of Project (30%) |
Project Topics and Datasets
Min will release a curated list of recommendation system datasets suitable for the project. You are expected to select one dataset and define a clear recommendation problem around it. Your project should include:
- A well-defined user problem and recommendation objective.
- At least one classical baseline (e.g., collaborative filtering, matrix factorization).
- At least one neural or advanced model.
- A rigorous offline evaluation using appropriate metrics (e.g., NDCG, Recall@K, MRR).
- An error analysis identifying failure modes.
- An ethical analysis addressing bias, fairness, privacy, or stakeholder impact.
Compute Resources
You may use the SoC Compute Cluster for your project work. Details on how to access the cluster will be provided in Canvas Announcements. You may also use Google Colab or other cloud platforms, but ensure your experiments are reproducible.
Peer Assessment
Due to the small cohort size, no peer review is required for this course. Project grades will be assessed directly by the teaching team based on the applicable final deliverable: the STePS presentation or the project report.
Academic Honesty for Projects
Group projects are collaborative by nature. However, each member is expected to contribute meaningfully. Please refer to the Grading page for the full academic honesty policy, including the No-Sponge Rule. AI tools may be used for the project but must be documented appropriately.