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

arXiv: https://arxiv.org/abs/2404.17238 · HTML: https://mlsanity.com/paper/2404.17238/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2024.

## Built on by (most cited first, 3 of 3)

- [Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in Heterogeneous Graphs](https://mlsanity.com/paper/2506.20980/) (2025)
- [Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment](https://mlsanity.com/paper/2605.00731/) (2026)
- [Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods](https://mlsanity.com/paper/2505.16466/) (2025)

## Abstract (arXiv)

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.