Year: 2023
Type: conference-paper
Abstract: Multimedia content is of predominance in the modern Web era. Many recommender models have been proposed to investigate how users interact with items which are represented in diverse modalities. In real scenarios, different modalities reveal different aspects of item attributes and usually possess different importance to user purchase decisions. However, it is difficult for models to figure out users' true preference towards different modalities since there exists strong statistical correlation between different modalities. Even worse, the strong statistical correlation might mislead models to learn the spurious preference towards inconsequential modalities. As a result, when data (modal features) distribution shifts, the learned spurious preference might not guarantee to be as effective on inference as on the training set.
Authors: Jinghao Zhang, Qiang Liu, Shu Wu, Liang Wang
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4384652102