Assignments
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 seminars, 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 final presentation. |
| Quizzes / Tests | 10% | Short checks on foundational concepts, metrics, and model design choices. |
| Final Exam | 30% | Individual final examination covering the full course. |
Submission Notes
- Submission channels, file formats, and deadlines 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.
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). Student groups should have 5 to 6 members and will be partially self-assembled (into initial subgroups of size 1 to 3) and then assembled by Min into final groups, with project preferences and expertise taken into account. 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 4–5 | Declare your initial subgroup (1–3 students), skills, and project interests via the Canvas survey. | — |
| 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 | Teams present their implemented recommender, evaluation results, error analysis, ethical analysis, and future improvements. | Part of Project (30%) |
| Final Project Report | Exam Period | Written report covering problem formulation, dataset preparation, baselines, model design, evaluation, error analysis, and ethical analysis. | 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 via TEAMMATES
This course uses TEAMMATES for intra-team peer assessment to evaluate individual contribution and calibrate project grades. There are two rounds:
- Formative Assessment (Week 7, ungraded): A mid-project peer review to give your team early feedback on contribution and collaboration. Results are for your team’s reflection only and do not affect your grade.
- Summative Assessment (Week 13, graded): A final peer review conducted at the end of the course. Results will be used to moderate individual project grades — outstanding contributors may receive a grade uplift, while those who have not contributed adequately may receive a reduced project grade.
You will receive an invitation to TEAMMATES via your NUS email. Please complete both assessments by their respective deadlines.
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.