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| A Two-tower Model for Multistage Literature Recommendation Incorporating Comparative Learning |
| Ye Guanghui, Tan Qitao, Wu Chuan, Song Xiaoying, Li Songye |
| School of Information Management, Central China Normal University, Wuhan 430079 |
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Abstract Literature recommendation is an important research topic in literature information mining. Effectively addressing the long-tail problem in traditional literature recommendation methods is difficult, as is adequately capturing high-dimensional literature features, leading to poor recommendation results. Therefore, a high-quality literature recommendation model is proposed in this study. A multistage dual-tower model incorporating contrastive learning is introduced to learn user and literature features utilizing user towers and literature towers, respectively. To improve the effectiveness of high-dimensional representation learning, a recall layer and a refined ranking layer are designed for multistage recommendations, with the recall layer used for irrelevant literature filtering and the refined ranking layer used for user-personalized recommendations. Experimental results on the CiteULike-a dataset show that the proposed model helps alleviate the long-tail distribution problem in literature recommendation. The model also performs better on the literature recommendation task than the single-stage model.
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Received: 10 January 2024
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