  With the increasing development of e-commerce and online services,
personalized recommendation systems have become crucial for enhancing user
satisfaction and driving business revenue. Traditional sequential
recommendation methods that rely on explicit item IDs encounter challenges in
handling item cold start and domain transfer problems. Recent approaches have
attempted to use modal features associated with items as a replacement for item
IDs, enabling the transfer of learned knowledge across different datasets.
However, these methods typically calculate the correlation between the model's
output and item embeddings, which may suffer from inconsistencies between
high-level feature vectors and low-level feature embeddings, thereby hindering
further model learning. To address this issue, we propose a dual-tower
retrieval architecture for sequence recommendation. In this architecture, the
predicted embedding from the user encoder is used to retrieve the generated
embedding from the item encoder, thereby alleviating the issue of inconsistent
feature levels. Moreover, in order to further improve the retrieval performance
of the model, we also propose a self-supervised multi-modal pretraining method
inspired by the consistency property of contrastive learning. This pretraining
method enables the model to align various feature combinations of items,
thereby effectively generalizing to diverse datasets with different item
features. We evaluate the proposed method on five publicly available datasets
and conduct extensive experiments. The results demonstrate significant
performance improvement of our method.
