Abstract
It is a common way to represent paper properties as a heterogeneous academic network graph, such as authorships, citations, by which the latent features of paper can be learnt. To better integrate both text and structural features, we propose the joint embedding method for paper recommendation. We adopt a pre-trained language model to learn the paper semantic features from titles, and adopt a graph convolution network to extract the structural features from the constructed academic network graph. These two embeddings are combined together through the attention mechanism as a joint one. To clarify the real negative samples on uncited papers, we introduce some expert rules as the selection strategy on samples in model training, which can exclude the far-unrelated negative samples and potential positive samples. User interests are modeled by their historical publications and references and thus papers are recommended according to the relatedness between user interests and paper embeddings. We conduct experiments on the ACM academic paper dataset. The results show that our model outperforms baseline methods on personalized recommendation. We also analyze the influence of model structure and parameter setting. The results show that our sample strategy effectively improves the precision of recommendation, which illustrate that the strategy enhances the quality of training data.
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Li, W., Xie, Y., Sun, Y. (2022). Joint Embedding Multiple Feature and Rule for Paper Recommendation. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1492. Springer, Singapore. https://doi.org/10.1007/978-981-19-4549-6_5
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