Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 3: Clear rejection
review_content: This paper proposes to incorporate relational information in metric learning, and experiments on MovieLens show that using both label and relational information can outperform that using only one single component in a K-NN classification setting.

I am not an expert in metric learning, but I find it really difficult to evaluate the effectiveness of the proposed approach given the results presented in the paper. I also feel that the paper writing is not clear enough and some details are missing.

First, I am not surprised that both entity (features of movies) and association (user ratings) information do help in predicting the type of the movies. But there aren’t any comparisons to other approaches or even discussions about related work.

Equation (1) only measures the link strength between two entities. For your Labels + Relations experiment, it is unclear how you put both constraints in Algorithm 1.

I think it is also necessary to specify what features (attributes) you used exactly. How many of them are numerical and how many are categorical (otherwise why set gamma = 0.5)? Also how do you pick the #constraints (13680, 36594)? Need to clarify.
review_title: review

final_score: rating: 4: Ok but not good enough - rejection
review_content: The authors proposed a new metric learning algorithm for learning from relational data. They argued that most metric learning algorithms focusing on learning from data represented by feature vectors and the proposed algorithm handles relational data. However, probably due to the page limitation --- I feel that the authors do not explain it very well on why we should use the propose algorithm.

First, the MovieLens dataset is a relatively popular dataset, so I think the readers would want to know the advantages of the proposed algorithm on this dataset compared to other algorithms. If there are no comparable algorithms in the proposed settings, then the authors should state the reasons of using the new settings. For example, list the new applications which can be enabled by the new settings. Finally, from Table 1 along, it is hard to tell if the empirical results of the proposed algorithm are strong or not. 

In short, I appreciate the fact that the authors want to propose a new method for metric learning. However, it is not clear what the main research question is in the paper. I recommend the author to improve the description and list the advantages of using the proposed algorithm in the next version of the paper.

review_title: review

Decision: Reject