With the explosively increasing of academic papers, efficient academic document retrieval is becoming an essential requirement for large-scale information retrieval systems. Inspired by the success of deep semantic hashing in normal document retrieval, deep semantic hashing is a promising approach for academic document retrieval by mapping academic documents into efficient hash codes. However, for academic document retrieval, the existing deep semantic hashing methods suffer from following two problems: (1) they cannot differentiate the importance of different field labels; (2) they cannot plenty utilize the structure information in paper citations. To address these problems, we propose a novel Large-scale Academic deep Semantic Hashing, called LASH. Specifically, LASH first treats paper citations as a citation network, and then employs a multi-input variational deep autoencoder to directly encode both structure information of the citation network and semantic information of academic documents into unified hash codes. Moreover, a weighted percentage similarity is designed to measure the importance of different field labels, which is a linear combination of Jaccard and Cosine similarity. Supervised by the similarity, the learned unified hash codes can further preserve the importance of different field labels. Extensive experiments show LASH significantly outperforms state-of-the-art baselines over proposed three real-world large-scale academic datasets.

Document hashing is a powerful paradigm for document retrieval, which maps high-dimensional documents to compact hashing codes with preserving the similarity of original data. While fairly successful, the existing document hashing methods do not consider the relevance relationship among different documents from a category and the hierarchical relationship among categories. Intuitively, the intra-category relevance connects related concepts among different documents, which can supplement the omitted information for each document; meanwhile the hierarchical categories can help to identify whether mistakes occur in leaf categories or parent categories, which can be used to reduce the mistakes occurring in parent categories that are often more serious. Inspired by above intuitions, we propose a novel \textbf{I}ntra-category aware \textbf{H}ierarchical supervised \textbf{D}ocument \textbf{H}ashing, called IHDH. Specifically, IHDH is a binary autoencoder architecture equipped with two novel components: intra-category component and hierarchy component. The intra-category component exploits the difference among latent semantic representations of different documents from a category to supplement the omitted information for each document. The hierarchy component utilizes the hierarchical structure to transform the probabilities of leaf categories into the probabilities of parent categories by union operation, and then gives a further parent-level penalty to reduce the mistakes occurring in parent categories.

Recently, deep supervised hashing methods have shown state-of-the-art performance by integrating feature learning and hash codes learning into an end-to-end network to generate high-quality hash codes. However, it is still a challenge to learn discriminative hash codes for preserving the label information of images efficiently. To overcome this difficulty, in this paper, we propose a novel Partial-Softmax Loss based Deep Hashing, called PSLDH, to generate high-quality hash codes. Specifically, PSLDH first trains a category hashing network to generate a discriminative hash code for each category, and the hash code will preserve semantic information of the corresponding category well. Then, instead of defining the similarity between datapairs using their corresponding label vectors, we directly use the learned hash codes of categories to supervise the learning process of image hashing network, and a novel Partial-SoftMax loss is proposed to optimize the image hashing network. By minimizing the novel Partial-SoftMax loss, the learned hash codes can preserve the label information of images sufficiently. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in image retrieval task.