Bayesian Preference Learning with the Mallows Model
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- Matematisk institutt [3781]
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.List of papers
Paper I: Qinghua Liu, Marta Crispino, Ida Scheel, Valeria Vitelli, and Arnoldo Frigessi (2019). “Model-based Learning from Preference data”. Annual review of statistics and its application. 6, s 329- 354. DOI: 10.1146/annurev-statistics-031017-100213. The article is not available in DUO due to publisher restrictions. The published version is available at: https://doi.org/10.1146/annurev-statistics-031017-100213 |
Paper II: Qinghua Liu, Andrew Henry Reiner, Arnoldo Frigessi and Ida Scheel (2019). “Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model”. Knowledge-Based Systems. 186, s 1-12. DOI: 10.1016/j.knosys.2019.104960. The article is included in the thesis. Also available at: https://doi.org/10.1016/j.knosys.2019.104960 |
Paper III: Qinghua Liu, Valeria Vitelli, Arnoldo Frigessi and Ida Scheel (2021). “Pseudo-Mallows for Efficient Preference Learning”. Manuscript. To be published. The paper is not available in DUO awaiting publishing. |
Paper IV: Øystein Sørensen, Marta Crispino, Qinghua Liu and Valeria Vitelli (2020). “BayesMallows: an R Package for the Bayesian Mallows Model”. The R Journal. 12(1), s 324- 342 DOI: 10.32614/RJ-2020-026. The article is included in the thesis. Also available at: https://doi.org/10.32614/RJ-2020-026 |