  Traditional recommender systems primarily leverage identity-based (ID)
representations for users and items, while the advent of pre-trained language
models (PLMs) has introduced rich semantic modeling of item descriptions.
However, PLMs often overlook the vital collaborative filtering signals, leading
to challenges in merging collaborative and semantic representation spaces and
fine-tuning semantic representations for better alignment with warm-start
conditions. Our work introduces CARec, a cutting-edge model that integrates
collaborative filtering with semantic representations, ensuring the alignment
of these representations within the semantic space while retaining key
semantics. Our experiments across four real-world datasets show significant
performance improvements. CARec's collaborative alignment approach also extends
its applicability to cold-start scenarios, where it demonstrates notable
enhancements in recommendation accuracy. The code will be available upon paper
acceptance.
