Neural Embedding Allocation:Distributed Representations of Topic Models
Table 4: Mean reciprocal rank for co-author retrieval.
['[EMPTY]', 'ATM', 'NEA embeddings', 'Tf-idf', 'NEA smoothing']
We also studied the performance of NEA for smoothing the author-topic distributions. The ATM could be used for a variety of applications such as automated reviewer recommendations Rosen-Zvi et al. Since these applications are based on searching for similar authors, we can treat them as a ranking problem. Following Rosen-Zvi et al. Using this distance metric, we searched for similar authors in the NIPS corpus for the 125 out of 2037 authors who wrote at least 5 papers. We reported the mean reciprocal rank (MRR) based on the rank of the most similar co-author. Further improvement was achieved by the NEA-smoothed version of the ATM’s parameters which also outperformed author vectors generated from a tf-idf baseline at this task.