Paper:
Bitcoin is a virtual coinage system that enables users to trade virtually free of a central trusted authority. All transactions on the Bitcoin blockchain are publicly available for viewing, yet as Bitcoin is built mainly for security its original structure does not allow for direct analysis of address transactions. Existing analysis methods of the Bitcoin blockchain can be complicated, computationally expensive or inaccurate. We propose a computationally efficient model to analyze bitcoin blockchain addresses and allow for their use with existing machine learning algorithms. We compare our approach against Multi Level Sequence Learners (MLSLs), one of the best performing models on bitcoin address data.

Review:
In this paper, the authors propose a new method for generating vector embeddings. The studied problem is important and the topic is related to area of Bitcoin. 
Empirical studies on some dataset (no description about the dataset) show some results on some evaluation metrics (no clear description about the metrics). For the methods 1-MLSL, 2-MLSL and 3-MLSL, it is seems that they are better than the proposed one (i.e., A2V) on some metrics, which is then inconsistent with the claims in the paper.
My major concern is that the paper is a bit too short and is lack of some necessary information, for example:
1 The authors are encouraged to provide sufficient background introduction, so that the reader can have a big picture of the problem and area.
2 The authors are encouraged to provide a detailed discussion and justification about the motivation, as well as the challenges and intuitions about the proposed method in the Introduction Section.
3 The authors are encouraged to show a detailed derivation about the technical details, in particular its difference compared with the major baseline, i.e., MLSL.
4 The authors are encouraged to follow the typical writing about the experiments in a paper, e.g., description about the datasets, evaluation metrics, baseline methods, parameter configurations and results analysis, etc. 
Based on the above comments, I have to make a reject recommendation.