TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal Content

TL;DR

TruthSR is a novel sequential recommender system that leverages user-generated multi-modal content while explicitly modeling content consistency and a trust mechanism to improve recommendation accuracy and robustness.

Contribution

It introduces a method to explicitly model the consistency and complementarity of multi-modal content and a trust mechanism combining subjective and objective perspectives.

Findings

Outperforms state-of-the-art methods on four datasets.

Effectively mitigates noise interference in multi-modal content.

Enhances modeling of user preferences through multi-modal data.

Abstract

Sequential recommender systems explore users' preferences and behavioral patterns from their historically generated data. Recently, researchers aim to improve sequential recommendation by utilizing massive user-generated multi-modal content, such as reviews, images, etc. This content often contains inevitable noise. Some studies attempt to reduce noise interference by suppressing cross-modal inconsistent information. However, they could potentially constrain the capturing of personalized user preferences. In addition, it is almost impossible to entirely eliminate noise in diverse user-generated multi-modal content. To solve these problems, we propose a trustworthy sequential recommendation method via noisy user-generated multi-modal content. Specifically, we explicitly capture the consistency and complementarity of user-generated multi-modal content to mitigate noise interference. We also achieve the modeling of the user's multi-modal sequential preferences. In addition, we design a trustworthy decision mechanism that integrates subjective user perspective and objective item perspective to dynamically evaluate the uncertainty of prediction results. Experimental evaluation on four widely-used datasets demonstrates the superior performance of our model compared to state-of-the-art methods. The code is released at https://github.com/FairyMeng/TrustSR.