Adaptive preference learning from multi-user feedback

Thumbnail Image

Files

master_Le_Duong_2025.pdf (655.91 KB)

URL

Journal Title

Journal ISSN

Volume Title

School of Science | Master's thesis
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

Date

2025-05-07

Department

Major/Subject

Machine Learning, Data Science and Artificial Intelligence

Mcode

Degree programme

Master's Programme in Computer, Communication and Information Sciences

Language

en

Pages

59

Series

Abstract

We consider the problem of multi-user preference learning, where the model learns from multiple human users and uses the learned information to quickly adapt to new users. Previous attempts at developing such a system assumed that users who are connected on platforms like social media tend to like similar products. This assumption is unlikely to hold in practice because users usually know only a small fraction of their connections. Other works imposed a structure on user preferences using shared latent functions. This method is limited by the representation capabilities of the chosen latent functions. In this work, we proposed a novel probabilistic approach to multi-user preference learning by partitioning user preferences into two components: an (optional) shared knowledge between users and a set of individual parameters. We trained a model on each user in the training user base and aggregated these trained models to extract both the shared and individual components simultaneously. The extracted information is stored using a Bayesian Neural Network, which facilitates efficient adaptation to new users. During learning and adaptation, we employed active learning to query items to users for labeling. We tested this framework on both synthetic and real preference datasets, and also applied the method to an existing retrosynthesis scoring system. The results show that our model outperformed even personalized models trained on the users in terms of recommending high-rating items to users.

Description

Supervisor

Kaski, Samuel

Thesis advisor

Guo, Yujia

Keywords

preferenc learning, multi-user preference learning, user adaptation, Bayesian neural network, knowledge extraction and combination, human-AI collaboration

Other note

Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202506174906

Collections

[dipl] Perustieteiden korkeakoulu / SCI