Leveraging multiple control codes for aspect-controllable related paper recommendation

Abstract

Aspect-Controllable Related Papers Recommendation (ACRPR) aims to satisfy users’ fine-grained needs for specific aspects when finding related papers. Existing approaches rely on the segmentation of texts or aspects to independently learn multi-aspect representations of papers. However, different aspects of a paper are guided by its overall theme and interconnected with intrinsic relevance. In light of this, we propose a simple yet effective ACRPR framework called mCTRL, which leverages multiple control codes in Transformer to simultaneously learn multiple aspect-specific paper representations. Specifically, mCTRL incorporates a $\left[\mathrm{CLS}\right]$ control code to capture the overall theme and multiple $\left[\mathrm{ASP}\right]$ control codes to exploit fine-grained aspect information. Additionally, we introduce a hierarchical loss function to balance the overall theme and various aspects of a paper, enabling their mutual enhancement and alignment. Extensive comparative experiments on real-world datasets demonstrate the superiority of our proposed method over previous state-of-the-art approaches. Evaluations are conducted on 5 backbone models and 5 dimensions, which confirm the generalization ability of mCTRL. Moreover, ablation studies and further analyses prove the effectiveness and efficiency of mCTRL and the specialization across aspects of generated embeddings.