Multimodal Recommendation via Self-Corrective Preference Alignmen

TL;DR

This paper introduces MSPA, a novel framework for personalized multimodal author recommendation in live streaming, which effectively aligns user preferences with author features to enhance accuracy and interpretability.

Contribution

The paper presents MSPA, a new multimodal recommendation framework that uses structured preference generation and self-corrective alignment to improve live streaming recommendations.

Findings

MSPA outperforms baseline models in accuracy and recall.

MSPA enhances interpretability of recommendations.

Visualizations confirm the effectiveness of preference alignment.

Abstract

With the rapid growth of live streaming platforms, personalized recommendation systems have become pivotal in improving user experience and driving platform revenue. The dynamic and multimodal nature of live streaming content (e.g., visual, audio, textual data) requires joint modeling of user behavior and multimodal features to capture evolving author characteristics. However, traditional methods relying on single-modal features or treating multimodal ones as supplementary struggle to align users' dynamic preferences with authors' multimodal attributes, limiting accuracy and interpretability. To address this, we propose MSPA (Multimodal Self-Corrective Preference Alignment), a personalized author recommendation framework with two components: (1) a Multimodal Preference Composer that uses MLLMs to generate structured preference text and embeddings from users' tipping history; and (2) a Self-Corrective Preference Alignment Recommender that aligns these preferences with authors' multimodal features to improve accuracy and interpretability. Extensive experiments and visualizations show that MSPA significantly improves accuracy, recall, and text quality, outperforming baselines in dynamic live streaming scenarios.