The most used recommendation method is collaborative filtering, and the key part of collaborative filtering is to compute the similarity. The similarity based on co-occurrence of similar event is easy to implement and can be applied to almost all the situation. So when the word2vec model reach the state-of-art at a lower computation cost in NLP. An correspond model in recommender system item2vec is proposed and reach state-of-art in recommender system. It is easy to see that the position of user and item is interchangeable when their count size gap is not too much, we proposed a user2vec model and show its performance. The similarity based on cooccurrence information suffers from cold start, we proposed a content based similarity model based on doc2vec which is another technology in NLP.
The idea of learning user embeddings for downstream tasks in recommender systems is a good one.
However, this paper proposes no significant methodological developments (e.g., user2vec is an extension of item2vec obtained by transposing the observation matrix). Further, it does not present a thorough study with interesting empirical results (doc2vec does not improve performance, a single dataset is used, baselines are not state of the art).
Overall, this short paper (3 pages + refs) seems a bit preliminary and, in its current state, does not make a significant enough contribution to be accepted at this venue.
I would suggest that a more thorough analysis of similarity methods for NN models could be interesting to a recsys workshop or perhaps a conference focussed on recsys (e.g., ACM recsys).