Manually selecting appropriate scholarly venues is becoming a tedious and time-consuming task for researchers due to many reasons that include relevance, scientific impact, and research visibility. Sometimes, high-quality papers get rejected due to mismatch between the area of the paper and the scope of the journal. Recommending appropriate academic venues can, therefore, enable researchers to identify and take part in relevant conferences and publish in journals that matter the most. A researcher may certainly know of a few leading venues for her specific field of interest. However, a venue recommendation system becomes particularly helpful when exploring a new domain or when more options are needed. Due to high dimensionality and sparsity of text data, and complex semantics of the natural language, journal identification presents difficult challenges. We propose a novel and unified architecture that contains Bi-directional LSTM (Bi-LSTM) and Hierarchical Attention Network (HAN) to address the above problems. We call the proposed architecture modularized Hierarchical Attention-based Scholarly Venue Recommender system (HASVRec), which only requires the abstract, title, keywords, field of study, and author of a new paper to recommend scholarly venues. Experiments on the DBLP-Citation-Network V11 dataset exhibit that our proposed approach outperforms several state-of-the-art methods in terms of accuracy, $F1$, nDCG, MRR, average venue quality, and stability.

由于许多原因，包括相关性，科学影响力和研究知名度，手动选择合适的学术场所已成为研究人员一项繁琐而耗时的任务。有时，高质量的论文会由于论文的面积和期刊范围不匹配而被拒绝。因此，推荐合适的学术场所可以使研究人员识别并参加相关的会议，并在最重要的期刊上发表论文。研究人员可能肯定会知道一些针对她特定兴趣领域的领先场所。但是，在探索新领域或需要更多选择时，场所推荐系统特别有用。由于文本数据的高度维度和稀疏性以及自然语言的复杂语义，期刊识别提出了艰巨的挑战。我们提出了一种新颖的，统一的体系结构，其中包含双向LSTM（Bi-LSTM）和分层注意力网络（HAN），以解决上述问题。我们将所提议的体系结构称为模块化的基于层次注意的学术场所推荐系统（HASVRec），该系统仅需要摘要，标题，关键词，研究领域以及新论文的作者来推荐学术场所。在DBLP-Citation-Network V11数据集上进行的实验表明，我们提出的方法在准确性，性能，我们将所提议的体系结构称为模块化的基于层次注意的学术场所推荐系统（HASVRec），该系统仅需要摘要，标题，关键词，研究领域以及新论文的作者来推荐学术场所。在DBLP-Citation-Network V11数据集上进行的实验表明，我们提出的方法在准确性，性能，我们将所提议的体系结构称为模块化的基于层次注意的学术场所推荐系统（HASVRec），该系统仅需要摘要，标题，关键词，研究领域以及新论文的作者来推荐学术场所。在DBLP-Citation-Network V11数据集上进行的实验表明，我们提出的方法在准确性，性能，$F\mathrm{1个}$，nDCG，MRR，平均会场质量和稳定性。