Enhancing Scholarly Paper Recommendation by Modelling Diversity of Research Interests

This study enhances scholarly paper recommendation by introducing a feature that captures the diversity of a researcher’s interests based on their past publications. Unlike existing weighting schemes that prioritize recent works, this approach considers content-wise relationships among papers. The feature is integrated into two weighting schemes and tested using Word2Vec text representations. Experiments on a dataset of 50 researchers show that while accuracy varies with parameters, optimal settings improve performance, as measured by NDCG@10 and P@10, compared to existing methods. This highlights the potential of diversity-aware interest modeling in scholarly recommender systems.

Recommended citation: Pan, X., Wang, S., Liu, T., van Ossenbruggen, J., de Boer, V., Huang, Z. (2024). Enhancing Scholarly Paper Recommendation by Modelling Diversity of Research Interests. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2024. Communications in Computer and Information Science, vol 2145. Springer, Singapore. https://doi.org/10.1007/978-981-97-5934-7_16
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