Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 7: Good paper, accept
review_content: The idea of jointly training a model to predict reviews and ratings is a nice one, and while it has been studied elsewhere a number of times, the main extension here is to use an LSTM RNN based model rather than previous work that has made use of topic models etc.

Positively, the idea is interesting and the comparisons to traditional work on matrix factorization are promising. The authors also consider issues of temporal evolution in the ratings, beating competitive baselines like TimeSVD++.

Negatively, it's not clear whether LSTMs are really beneficial here over "traditional" rating+text methods. No quantitative comparison to another rating+text method is provided, though many are discussed. Text prediction is discussed in terms of perplexity, but again this is not quantitatively or qualitatively compared to alternatives.

Overall though, I think the paper is worth accepting due to the overall promise of the idea, even if the experiments are not quite convincing yet.
review_title: null

final_score: rating: 10: Top 5% of accepted papers, seminal paper
review_content: This paper proposed a joint model for rate prediction and text generation.  The author compared the methods on a more realistic time based split setting, which requires “predict into the future.”  The model is interesting and sound, and I think it should be accepted to ICLR workshop
review_title: null

Decision: Accept