1. VOPRec: Vector Representation Learning of Papers with Text Information and Structural Identity for Recommendation
- Author
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Mengyi Mao, Jiaying Liu, Xiangjie Kong, Bo Xu, and Wei Wang
- Subjects
Word embedding ,Information retrieval ,Computer science ,business.industry ,Big data ,02 engineering and technology ,Recommender system ,Computer Science Applications ,Human-Computer Interaction ,Citation analysis ,020204 information systems ,Similarity (psychology) ,0202 electrical engineering, electronic engineering, information engineering ,Computer Science (miscellaneous) ,Identity (object-oriented programming) ,020201 artificial intelligence & image processing ,Learning to rank ,business ,Feature learning ,Information Systems - Abstract
Finding relevant papers is a non-trivial problem for scholars due to the tremendous amount of academic information in the era of scholarly big data. Scientific paper recommendation systems have been developed to solve such problem by recommending relevant papers to scholars. However, previous paper recommendations calculate paper similarity based on hand-engineered features which are inflexible. To address this problem, we develop a scientific paper recommendation system, namely VOPRec, by vector representation learning of paper in citation networks. VOPRec takes advantages of recent research in both text and network representation learning for unsupervised feature design. In VOPRec, the text information is represented with word embedding to find papers of similar research interest. Then, the structural identity is converted into vectors to find papers of similar network topology. After bridging text information and structural identity with the citation network, vector representation of paper can be learned with network embedding. Finally, top- $Q$ Q recommendation list is generated based on the similarity calculated with paper vectors. Through the APS data set, we show that VOPRec outperforms state-of-the-art paper recommendation baselines measured by precision, recall, F1, and NDCG.
- Published
- 2021