Disputation: Qinghua Liu

Doctoral candidate Qinghua Liu at the Department of Mathematics, Faculty of Mathematics and Natural Sciences, is  defending the thesis Bayesian Preference Learning with the Mallows Model for the degree of Philosophiae Doctor.

Picture of the candidate.

Doctoral candidate Qinghua Liu

The PhD defence will be partially digital, in room 720, Niels Henrik Abels hus and streamed directly using Zoom. The trial lecture will be prerecorded and available from 27th October. The host of the session will moderate the technicalities while the chair of the defence will moderate the disputation.

Ex auditorio questions: the chair of the defence will invite the audience to ask questions ex auditorio at the end of the defence. If you would like to ask a question, click 'Raise hand' and wait to be unmuted.

Trial lecture

"Latent variable modelling for ranking data"

Main research findings

In our modern society, with the bourgeoning of e-commerce and online streaming platforms, customers are overwhelmed by the choices. One important approach to solve this problem is recommender systems. Recommender systems learn customers' preferences based on their past interactions with the website/platform, as well as the interactions data of other customers, to eventually provide a list of recommendations that is relevant to the customer.

In this work, the author studied the use of statistical models to learn customers' preferences, with a focus on the Bayesian Mallows Model. The author provided a new approach to learn personal preferences and make personalised recommendations from clicking data. Through experimentation, it was illustrated that the proposed method achieved good balance between recommending items that are closely related to what the customers previously interacted with, while not overlooking the issue of recommendation diversity: that is, recommending the items that are interesting, novel and surprising to the customer. The author also provided a new approach to achieve more computationally efficient preference learning.

Published Oct. 14, 2021 4:08 PM - Last modified Nov. 5, 2021 10:06 AM