VECTORIZATION METHODS IN RECOMMENDER SYSTEM

Abstract: 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.

Reviews and author responses:

Review: 

— the writing is not sufficiently clear and a lot of the ideas are hard to follow (the sections 3.2 and 3.3 which should cover proposed methods are only a paragraph long each, have no loss functions and no architecture descriptions/diagrams)
— the ideas presented are only derivative and are not sufficiently novel for the venue
— the experimental section is incomplete having results on one dataset and not enough state-of-the art baselines. the uplfits look small and there is no discussion on statistical significance

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).


The paper studies the use of embedding techniques in recommender systems, and shows that item2vec (an item vectorization method) can be replaced by user2vec, as users and items are interchangeable.

This is a reasonable enough idea, though not sufficient for publication in ICLR. I'd suggest the authors address the following details:
-- The methodological contribution is too small, and fairly obvious. Not sufficient for this conference.
-- Only evaluated on one dataset, so unclear whether the results are really representative
-- Comparisons against a very limited set of similar methods, which are probably not state-of-the-art for this dataset
-- The results don't seem significant, all methods compared perform almost equally

Meta-review: The reviewers are unanonymous in their assessment that the paper is not ICLR quality in its current form.

Decision: rejected-papers