| Course Code | COMP9727 |
| Course Title | Recommender Systems |
| Convenor |
Lina Yao
|
| Admin |
Xiaocong Chen
|
| Course Email | cs9727@cse.unsw.edu.au |
| Classes |
Lectures^
:
Monday 16:00 - 18:00 Webster Theatre B Wednesday 16:00 - 18:00 Webster Theatre B Tutorials : Wednesday 11:00 - 12:00 Goldstein G07 Wednesday 12:00 - 13:00 Goldstein G07 Wednesday 13:00 - 14:00 Goldstein G07 Wednesday 14:00 - 15:00 Online* *The online class is only available to those students who are oversea. ^ For those students who are not able to attend lecture in-person due to oversea, you can watch the lecture recording via Moodle |
| Units of Credit | 6 |
| Handbook Entry |
UG:
https://www.handbook.unsw.edu.au/undergraduate/courses/current/COMP9727.html
PG: https://www.handbook.unsw.edu.au/postgraduate/courses/current/COMP9727.html |
The goal of this course is to educate students in the recommender system. A r ecommender system (RS) is a useful information filtering and search tool for guiding users in a personalized way of discovering products/services from a large space of possible options. It is one of the most important and popular machine learning applications. They are primarily used in commercial applications like Amazon, Netflix, Tik-Tok and then extend to many sectors like cyber security, transportation and health informatics. As such, it has become a vital and indispensable component in the modern internet industry.
This course aims to systematically introduce the fundamental techniques related to recommendation systems include collaborative filtering, matrix factorization and more advanced and up-to-date techniques like deep learning, reinforcement learning. Students will gain the techniques which are essential and important for real-world application.
Before commencing this course, students should:
Knowledge of neural networks, machine learning are desirable.
After completing this course, students will:
This course contributes to the development of the following graduate capabilities:
| Graduate Capability | Acquired in |
| Scholars capable of independent and collaborative enquiry, rigorous in their analysis, critique, and reflection, and able to innovate by applying their knowledge and skills to the solution of the novel as well as routine problems. |
assignments, prac work
|
| Entrepreneurial leaders capable of initiating and embracing innovation and change, as well as engaging and enabling others to contribute to change |
-
|
| Professionals capable of ethical, self-directed practice and independent lifelong learning |
assignments, prac work
|
| Global citizens who are culturally adept and capable of respecting diversity and acting in a socially just and responsible way | - |
Learning will be largely facilitated through the delivery of lectures. The tutorials will provide an opportunity to reinforce students’ understanding of the concepts discussed in the lectures. The assignments will help in the development of problem-solving skills. The final exam will assess students’ understanding of the whole course and the problem-solving skills.
The Student Code of Conduct ( Information , Policy ) sets out what the University expects from students as members of the UNSW community. As well as the learning, teaching and research environment, the University aims to provide an environment that enables students to achieve their full potential and to provide an experience consistent with the University's values and guiding principles. A condition of enrolment is that students inform themselves of the University's rules and policies affecting them, and conduct themselves accordingly.
In particular, students have the responsibility to observe standards of equity and respect in dealing with every member of the University community. This applies to all activities on UNSW premises and all external activities related to study and research. This includes behaviour in person as well as behaviour on social media, for example Facebook groups set up for the purpose of discussing UNSW courses or course work. Behaviour that is considered in breach of the Student Code Policy as discriminatory, sexually inappropriate, bullying, harassing, invading another's privacy or causing any person to fear for their personal safety is serious misconduct and can lead to severe penalties, including suspension or exclusion from UNSW.
If you have any concerns, you may raise them with your lecturer, or approach the School Ethics Officer , Grievance Officer , or one of the student representatives.
Plagiarism is defined as using the words or ideas of others and presenting them as your own. UNSW and CSE treat plagiarism as academic misconduct, which means that it carries penalties as severe as being excluded from further study at UNSW. There are several on-line sources to help you understand what plagiarism is and how it is dealt with at UNSW:
Make sure that you read and understand these. Ignorance is not accepted as an excuse for plagiarism. In particular, you are also responsible that your assignment files are not accessible by anyone but you by setting the correct permissions in your CSE directory and code repository, if using. Note also that plagiarism includes paying or asking another person to do a piece of work for you and then submitting it as your own work.
UNSW has an ongoing commitment to fostering a culture of learning informed by academic integrity. All UNSW staff and students have a responsibility to adhere to this principle of academic integrity. Plagiarism undermines academic integrity and is not tolerated at UNSW. Plagiarism at UNSW is defined as using the words or ideas of others and passing them off as your own.
If you haven't done so yet, please take the time to read the full text of
The pages below describe the policies and procedures in more detail:
| Item | Topics | Due | Marks | Contributes to |
| Assignment 1 |
Foundations of Recommendation Systems
|
Week 5 | 25% | CLO 1, 3, 4 |
| Assignment 2 |
Advanced Recommendation Model Design and Analysis
|
Week 10 | 25% | CLO 1-4 |
| Final Exam | All topics | Exam period | 50% | CLO 1-4 |
*All assignments are individual.
Exam Hurdle: 20/50. Failing to pass the hurdle will lead to 45 FL.
| Week | Lectures | Tutes (Tentative) | Assignments | Notes |
| 1 |
1.1 Course intro, Assessments, the background of Recommender Systems
1.2 Collaborative Filtering: Memory-Based methods |
Basic Concepts in Machine Learning | - | - |
| 2 |
Model-Based Collaborative Filtering
|
CF |
Ass 1 release
|
- |
| 3 | Learning to rank | MF, associate rules | - | - |
| 4 |
4.1 Content-based Recommender systems
4.2 Group Recommender systems |
bayesian and rank | - | - |
| 5 |
5.1 Sequential Recommender Systems
5.2 Guest Lectures from Apple Inc. and Raiz Investment |
Content-based recommendation |
Ass1 due
|
Census Day |
| 6 | No Lectures | No Tutes | - | Flexible Week |
| 7 |
Deep learning empowered Recommender Systems
|
Assignment 1 Review |
Ass2 release
|
- |
| 8 | Deep learning empowered Recommender Systems | Deep learning based RS | - | - |
| 9 |
Deep reinforcement learning for sequential recommendation
|
Reinforced RS | - | - |
| 10 |
10.1 Emerging topics in Recommender Systems
10.2 Exam review and wrap up |
Review and sample exam | Ass2 due | - |
No textbook will be used in this course. We'll provide the relevant resource after each lecture.
This course is evaluated each session using the myExperience system.
Resource created Tuesday 04 January 2022, 03:26:41 PM, last modified Tuesday 24 May 2022, 01:52:56 PM.