实验室博士研究生谢翌同学的论文“Learning Domain Semantics and Cross-Domain Correlations for Paper Recommendation”（作者：谢翌，孙宇清，Elisa Bertino）被SIGIR 2021会议录用。

第44届国际计算机学会信息检索大会（The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021）计划于2021年7月11日-7月15日以线上会议形式召开。SIGIR是人工智能领域信息检索方向的旗舰国际会议，专注于文本推荐、检索、语义计算等领域的新研究成果、新系统和新技术。

Abstract: Understanding how knowledge is technically transferred across academic disciplines is very relevant for understanding and facilitating innovation. There are two challenges for this purpose, namely the semantic ambiguity and the asymmetric influence across disciplines. In this paper we investigate knowledge propagation and characterize semantic correlations for cross discipline paper recommendation. We adopt a generative model to represent a paper content as the probabilistic association with an existing hierarchically classified discipline to reduce the ambiguity of word semantics. The semantic correlation across disciplines is represented by an influence function, a correlation metric, and a ranking mechanism. Then a user interest is represented as a probabilistic distribution over the target domain semantics and the correlated papers are recommended. Experimental results on real datasets show the effectiveness of our methods. We also discuss the intrinsic factors of results in an interpretable way. Compared with traditional word embedding based methods, our approach supports the evolution of domain semantics that accordingly lead to the update of semantic correlation. Another advantage of our approach is its flexibility and uniformity in supporting user interest specifications by either a list of papers or a query of key words, which is suited for practical scenarios.

论文概述：理解知识如何在不同学科之间传播，对于促进学术创新非常重要。实现这一目标要面临两个挑战，即语义歧义和跨学科的不对称学术影响。本文研究了跨学科论文推荐中的知识传播和语义关联特征。我们采用生成模型将论文内容表示为在现有层次化学科分类上的概率关联，以降低语义的歧义。学科间的语义相关性通过影响函数、相关性度量和排序机制来表示。将用户兴趣表示为目标领域语义上的概率分布，并推荐相关论文。在真实数据集上的实验结果表明了该方法的有效性。我们基于可解释性实验讨论了结果的内在因素。与传统的基于词嵌入方法相比，本文方法支持领域语义的演化，从而支持语义关联的更新。本文方法的另一优点是它的灵活性和一致性，可以通过论文列表或关键字查询来支持用户兴趣表示，适用于实际应用场景。

引用：Yi Xie, Yuqing Sun*, Elisa Bertino.  Learning Domain Semantics and Cross-Domain Correlations for Paper Recommendation[C].  The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'21). July 2021.[pdf]