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Personalized Question Routing via Heterogeneous Network Embedding

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Figure

Table 1: Frequently used notations.
Figure 2: The pipeline of NeRank. There are two steps in the training procedure. Step 1 learns the entity embeddings usingan LSTM-equipped metapath-based network embedding algorithm
Table 2: Statistics of the datasets. r, q, and a represent ques-tion raiser, question, and question answerer.
Table 3: The comparisons of MRR, Hit@K, and Prec@1 be-tween NeRank and three baseline models.
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