Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author’s local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue–venue interaction. To solve this problem, we propose an author topic model–enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues’ capacity of exerting topic influence on other venues. The top-susceptibility captures venues’ propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-the-art methods.

中文翻译：

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场馆主题模型——学术大数据引文推荐的增强联合图建模

自然语言处理技术（例如主题模型）已被证明对具有处理内容信息能力的学术推荐任务有效。最近，由于出版地点数量空前，地点推荐正成为一项越来越重要的研究任务。然而，传统方法要么关注作者的本地网络，要么关注作者与场地的相似性，而忽略了学者与场地之间的多重关系，尤其是场地与场地的互动。为了解决这个问题，我们提出了一种作者主题模型增强的联合图建模方法，该方法由场地主题建模、特定场地的主题影响建模和学者偏好建模组成。我们首先使用潜在狄利克雷分配对场地主题进行建模。然后，我们通过考虑场地之间的主题相似性、场地的顶级影响力和场地的顶级敏感性，以非对称和低维的方式对特定场地的主题影响进行建模。顶级影响力表征场馆对其他场馆施加话题影响的能力。最高易感性捕捉场地受其他场地局部影响的倾向。对两个真实世界数据集的广泛实验表明，我们提出的联合图建模方法优于最先进的方法。最高易感性捕捉场地受其他场地局部影响的倾向。对两个真实世界数据集的广泛实验表明，我们提出的联合图建模方法优于最先进的方法。最高易感性捕捉场地受其他场地局部影响的倾向。对两个真实世界数据集的广泛实验表明，我们提出的联合图建模方法优于最先进的方法。