Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings
Table 2. Overall evaluation results.
['measure', 'P@5', 'P@10', 'P@20', 'NDCG@5', 'NDCG@10', 'NDCG@20', 'MAP', 'bpref']
Overall Results Analysis. This result agrees with our argument that the task of expert finding is different from information retrieval and the network structure plays an important role. It can be partially explained by that for a candidate c and a query q the ranking score can be (loosely) interpreted as scaling with #(c,q)/(#c#q) More specifically, a candidate with only one paper on q is likely to be ranked topmost.