Hierarchical Bayesian Modeling for Clustering Sparse Sequences in the Context of Group Profiling

Abstract: This paper proposes a hierarchical Bayesian model for clustering sparse sequences.This is a mixture model and does not need the data to be represented by a Gaussian mixture and that gives significant modelling freedom.It also generates a very interpretable profile for the discovered latent groups.The data that was used for the work have been contributed by a restaurant loyalty program company. The data is a collection of sparse sequences where each entry of each sequence is the number of user visits of one week to some restaurant. This algorithm successfully clustered the data and calculated the expected user affiliation in each cluster.

Reviews and author responses:

Pros:
-- Clustering sequence vectors is a practical and useful problem. Some of the business use-cases described in the paper are indeed useful and relevant for analytics in healthcare and retail.

Cons:
-- The paper is poorly written. There are numerous typos and grammatical errors throughout the paper. 
-- The ideas are not presented coherently. The writing needs to improve quite a bit to get accepted at a conference like ICLR.
-- Description of related literature is done very poorly.  
-- The generative model described clearly lacks justification. The model is not described concretely either. There is no clear description of the inference techniques used.
-- Empirical results are weak. 

The problem formulation at the bottom of page 3 correspond to what a bag of words preprocessing of a document would provide and in this the clustering would be a much simpler solution that just doing LDA.

The paper has zero interest.

The paper is very poorly written. It is hard to understand what the real contribution is in this paper. 
The connection of the model with HMM is not clear. The literature review has to be rewritten.

To the reader, it sounds that the authors are confused with the fundamentals itself: mixture model, Bayesian models, inference. 

> Mixture models can be based on any of the exponential family distributions - Gaussian just happens to be the most commonly used.
> Again if this is a Bayesian model, why are #clusters not inferred? The authors further mention that in their Pystan implementation K clusters were spun too quick. What was the K used here? Was it set to a very large value or just 3? Did the authors eventually use the truncated infinite mixture model in Pystan?
> The authors mention their model is conceptually similar to EM but then end up using NUTS. 
> Why is a url given in Section 2.3 instead of being given in the references? 
> Provide a plate model describing Section 3.2.

This paper propose a hierarchical Bayesian model to cluster sparse sequences data. The observations are modeled as Poisson distributions, whose rate parameter \lambda_i is written as the summation of \lambda_{ik}, a Gamma distribution with rate equal to the mixture proportion \alpha_{ik}. The model is implemented in Pystan. Experimental results on a real-world user visit dataset were presented.

The format of this paper, including the listing in the introduction section, the long url in section 2.3, and the model specification in section 3.2, can be improved. In particular, the presentation of the model would be more clear if the graphical model can be specified. 

The motivation of choosing the observation model and priors is not clear. In section 3, the author described the details of model specification without explaining why those design choices were appropriate for modeling sparse sequence data.

Experimental results on a real-world dataset is presented. However, to demonstrate how the model works, it would be best to add synthetic experiments as sanity check. Results using common baseline approaches should also be presented. The results should also be properly quantified in order to compare the relative advantage of different approaches.

The paper discusses clustering sparse sequences using some mixture model. It discusses results about clustering data obtained from a restaurant loyalty program.

It is not clear to me what the research contribution of the paper is. What I see is that some known techniques were used to cluster the loyalty program data and some properties of the experiments conducted noted down. No comparisons are made. I am not sure what to evaluate in this paper. 

Meta-review: All reviewers agree to reject. While there were many positive points to this work, reviewers believed that it was not yet ready for acceptance.

Decision: rejected-papers