Collaborative Semantic Alignment in Recommendation Systems

Abstract

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.

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