Scientific Literature Retrieval and Recommendation Model Based on RoBERTa and SASRec

Authors: Yuhui Zhang
Conference: ICIC 2025 Posters, Ningbo, China, July 26-29, 2025
Pages: 3602-3613
Keywords: Literature Retrieval and Recommendation RoBERTa FastBERT SASRec Sequential Recommendation

Abstract

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.
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