Cross-domain collaboration recommendation
Cross-domain collaboration recommendation
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DOI：
10.1145/2339530.2339730
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发表时间：
2012-08
期刊：
影响因子：
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通讯作者：
Jie Tang;Sen Wu;Jimeng Sun;Hang Su
中科院分区：
文献类型：
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作者：
Jie Tang;Sen Wu;Jimeng Sun;Hang Su
跨学科的合作对社会产生了巨大的影响。然而，研究人员往往很难建立这种跨领域的合作。跨域协作的模式是什么？这些合作是如何形成的？我们能预测这种合作吗？跨域协作与同一领域中的传统协作相比表现出非常不同的模式：1）稀疏连接：跨域协作很少; 2）互补专业知识：跨域协作者通常具有不同的专业知识和兴趣; 3）主题偏斜：跨域协作主题集中在主题的子集上。所有这些模式都违反了传统推荐系统的基本假设。在本文中，我们分析了研究出版物中的跨领域合作数据，并证实了上述模式。我们提出了跨领域主题学习（CTL）模型来解决这些挑战。为了处理稀疏连接，CTL通过主题层而不是作者层来整合现有的跨域协作，这解决了稀疏问题。为了处理互补的专业知识，CTL模型的主题分布从源和目标领域分开，以及跨领域的相关性。为了处理主题偏斜，CTL只对跨域协作的相关主题进行建模。我们比较了CTL与几种基线方法对来自不同领域的大型出版物数据集。CTL在多个推荐指标上显著优于基线。除了准确的推荐性能，CTL也是不敏感的参数调整中确认的敏感性分析。
Interdisciplinary collaborations have generated huge impact to society. However, it is often hard for researchers to establish such cross-domain collaborations. What are the patterns of cross-domain collaborations? How do those collaborations form? Can we predict this type of collaborations? Cross-domain collaborations exhibit very different patterns compared to traditional collaborations in the same domain: 1) sparse connection: cross-domain collaborations are rare; 2) complementary expertise: cross-domain collaborators often have different expertise and interest; 3) topic skewness: cross-domain collaboration topics are focused on a subset of topics. All these patterns violate fundamental assumptions of traditional recommendation systems. In this paper, we analyze the cross-domain collaboration data from research publications and confirm the above patterns. We propose the Cross-domain Topic Learning (CTL) model to address these challenges. For handling sparse connections, CTL consolidates the existing cross-domain collaborations through topic layers instead of at author layers, which alleviates the sparseness issue. For handling complementary expertise, CTL models topic distributions from source and target domains separately, as well as the correlation across domains. For handling topic skewness, CTL only models relevant topics to the cross-domain collaboration. We compare CTL with several baseline approaches on large publication datasets from different domains. CTL outperforms baselines significantly on multiple recommendation metrics. Beyond accurate recommendation performance, CTL is also insensitive to parameter tuning as confirmed in the sensitivity analysis.