  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.
