Scientific Literature Retrieval and Recommendation Model Based on RoBERTa and SASRec

With the rapid growth in the quantity and variety of scientific literature, efficiently retrieving and recommending relevant documents for researchers has become a challenge. This paper proposes a scientific literature retrieval and recommendation model integrating Robustly Optimized BERT Pretraining Approach RoBERTa and Self-Attentive Sequential Recommendation SASRec . By incorporating semantic feature information extracted by the RoBERTa model and domain category information predicted by the FastBERT model and combining traditional self-attention sequence recommendation models with proxy attention mechanisms and learnable filtering encoders, the model effectively captures the long-term dependencies of user behavior. This enhances the accuracy of scientific literature retrieval and recommendation. Experimental results demonstrate that the proposed model outperforms traditional methods regarding retrieval and recommendation accuracy, personalization, and efficiency.