Align Sequential Collaborative Signals and Text Semantics for Citation Recommendation: A Hybrid Perspective

The proliferation of online publications and interdisciplinary studies has presented researchers with the challenge of sifting through a substantial volume of articles to identify citations that substantiate their research ideas. Consequently, the development of citation recommendation technology has become a pivotal aspect of product promotion and marketing for academic support platforms. Traditionally, citation recommendation has primarily relied either on collaborative signals derived from paper interactions or on content-based similarity—both of which are essential for identifying relevant references. However, existing basic strategies often focus on one of these aspects while neglecting the other, leading to suboptimal performance in capturing the complex factors behind citation behavior. The reason is that the integration of domain characteristics in scholarly fields and the mining of semantic relevance in text information is also crucial for modelling researchers’ preferences. In this work, we present a novel citation recommendation model called SCTRec, that aligns S equential C ollaborative signals from publications’ indexes, i.e., IDs, and T ext semantic content for citation Rec ommendation. To address the actual technical challenges encountered, such as shifts in user preferences and semantic gaps between IDs and texts, we have designed a hybrid enhancement mechanism that bridges these semantic gaps, thereby learning more discriminative feature representations. The effectiveness of SCTRec in enhancing citation recommendation performance is substantiated by extensive experimental evaluation on multiple public datasets. The code is available on Anonymous Github at https://github.com/guaiqihen/SCTRec .