PhD scholarship: Meeting challenges of big data for cost-effective, scalable, robust and real-time recommender systems

Summary

Enrolment status New students
Student type Domestic students
Level of study Higher Degree by Research
Study area Engineering and Computing
HDR funding type Living stipend scholarship
Scholarship value $28,597 per annum (2021 rate), indexed annually
Scholarship duration Three years with the possibility of two 6-month extensions in approved circumstances
Opening date 25 January 2021
Closing date 15 March 2021

Description

Supervisor - Dr Hongzhi Yin

This project aims to systematically study how to meet emerging challenges from “4Vs (Volume, Veracity, Variety, Velocity)” of big data and develop a scalable, robust and real-time recommender system framework in a cost-effective and end-to-end manner. Specifically, our goal consists of four subtasks:

  1. developing a compact latent factor model for scalable recommendation;
  2. developing an anti-shilling model for secure recommendation;
  3. developing a heterogeneous feature embedding and fusion framework to enhance the robustness for cold-start recommendation;
  4. developing a meta learningbased online learning scheme to support streaming recommendation.

A working knowledge of deriving state-of-the-art machine learning approaches for real-world applications and publishing conference/journal papers on prestigious venues would be of benefit to someone working on this project.

School of Information Technology & Electrical Engineering

The Responsible Big Data Intelligence Lab (RBDI) is based in the School of ITEE, The University of Queensland. RBDI Lab aims and strives to develop energy-efficient, privacy-preserving, robust, explainable and fair data mining and machine learning techniques with theoretical backbones to better discover actionable patterns and intelligence from large-scale, heterogeneous, networked, dynamic and sparse data. RBDI joins forces with other fields such as urban transportation, healthcare, agriculture, E-commerce and marketing to help solve societal, environmental and economical challenges facing humanity, in pursuit of a sustainable future.

Eligibility

To be eligible, you must meet the entry requirements for a higher degree by research.

This scholarship is open to Australian citizens, permanent residents and International students who are currently in Australia.

Applications are closed.

Before you get started

If this scholarship has rules, download and read them.

How to apply

To apply for admission and scholarship, follow the link on the upper right of this page. There is no separate application for scholarship because you will have the opportunity to request scholarship consideration on the application for admission.

Before submitting an application you should:

When you apply, please ensure that under the scholarships and collaborative study section you:

  1. Select ‘My higher degree is not collaborative’
  2. Select 'I am applying for, or have been awarded a scholarship or sponsorship'.
  3. Select ‘Other’, then ‘Research Project Scholarship’ and type in ‘RECOMMENDER-YINin the 'Name of scholarship' field.

See an example of what you have to do

Learn more about applying for a higher degree by research at UQ

Selection criteria

Applications will be judged on a competitive basis taking into account the applicant’s previous academic record, publication record, honours and awards, and employment history.

The applicant will demonstrate academic achievement in the field/s of machine learning, recommender systems, data mining, and information retrieval and the potential for scholastic success.

A background or knowledge of model compression and natural language processing is highly desirable.

Applications are closed.

Contact

Dr Hongzhi Yin
Applications are closed.

Terms and conditions

Read the policy on UQ Research Scholarships.

A domestic part-time student with carer’s responsibilities, a medical condition or a disability, which prevents them from studying full time may be eligible for scholarship consideration, on a case by case basis.

Applications are closed.