VECTORIZATION METHODS IN RECOMMENDER SYSTEM
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 co-occurrence 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).
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
VECTORIZATION METHODS IN RECOMMENDER SYSTEM
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 co-occurrence information suffers from cold start, we proposed a content based similarity model based on doc2vec which is another technology in NLP.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 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).
assistant
 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).