In the extensive recommender systems literature, novelty and diversity have been identified as key properties of useful recommendations. However, these properties have received limited attention in the specific sub-field of research paper recommender systems. In this work, we argue for the importance of offering novel and diverse research paper recommendations to scientists. This approach aims to reduce siloed reading, break down filter bubbles, and promote interdisciplinary research. We propose a novel framework for evaluating the novelty and diversity of research paper recommendations that leverages methods from network analysis and natural language processing. Using this framework, we show that the choice of representational method within a larger research paper recommendation system can have a measurable impact on the nature of downstream recommendations, specifically on their novelty and diversity. We introduce a novel paper embedding method, which we demonstrate offers more innovative and diverse recommendations without sacrificing precision, compared to other state-of-the-art baselines.

翻译：在广泛的推荐系统文献中，新颖性和多样性已被确定为有用推荐的关键属性。然而，这些属性在研究论文推荐系统这一特定子领域中受到的关注有限。在本工作中，我们论证了为科学家提供新颖且多样化的研究论文推荐的重要性。这种方法旨在减少孤立阅读、打破过滤气泡，并促进跨学科研究。我们提出了一种新颖的评估研究论文推荐新颖性与多样性的框架，该框架利用了网络分析和自然语言处理的方法。通过该框架，我们证明，在大型研究论文推荐系统中，表征方法的选择会对下游推荐的性质——尤其是新颖性与多样性——产生可衡量的影响。我们引入了一种新颖的论文嵌入方法，该方法相比其他最先进的基线方法，能够在不牺牲精确率的前提下，提供更具创新性和多样性的推荐。