Multimedia recommender systems focus on utilizing behavioral information and content information to model user preferences. Typically, it employs pre-trained feature encoders to extract content features, then fuses them with behavioral features. However, pre-trained feature encoders often extract features from the entire content simultaneously, including excessive preference-irrelevant details. We speculate that it may result in the extracted features not containing sufficient features to accurately reflect user preferences. To verify our hypothesis, we introduce an attribution analysis method for visually and intuitively analyzing the content features. The results indicate that certain products' content features exhibit the issues of information drift}and information omission,reducing the expressive ability of features. Building upon this finding, we propose an effective and efficient general Behavior-driven Feature Adapter (BeFA) to tackle these issues. This adapter reconstructs the content feature with the guidance of behavioral information, enabling content features accurately reflecting user preferences. Extensive experiments demonstrate the effectiveness of the adapter across all multimedia recommendation methods. The code will be publicly available upon the paper's acceptance.

翻译：多媒体推荐系统致力于利用行为信息和内容信息来建模用户偏好。通常，系统采用预训练的特征编码器提取内容特征，然后将其与行为特征进行融合。然而，预训练特征编码器往往同时从整个内容中提取特征，其中包含了过多的与偏好无关的细节。我们推测这可能导致提取的特征未能包含足够准确反映用户偏好的信息。为验证这一假设，我们引入了一种归因分析方法，用于视觉化且直观地分析内容特征。结果表明，某些产品的内容特征存在信息漂移和信息缺失问题，从而降低了特征的表达能力。基于这一发现，我们提出了一种高效且通用的行为驱动特征适配器（BeFA）来解决这些问题。该适配器在行为信息的引导下重构内容特征，使得内容特征能够准确反映用户偏好。大量实验证明，该适配器在所有多媒体推荐方法中均表现出有效性。代码将在论文被接受后公开。