Bayesian Preference Learning with the Mallows Model

Abstract

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.