This paper proposes a sparse sequence clustering algorithm based on hierarchical Bayesian Modeling. This algorithm can cluster very sparse non-negative integer sequences of unequal length which do not necessarily belong to well-defined classes. Such Sequences are generated from the vast majority of normal human actions, for example, user behavior data for Wikipedia contributors expressed as a count of updates per day. Thus this algorithm and modeling technique is very useful for modeling non-negative integer sequences generated by real-life human actions, such as user-visits to websites or shops. This data model is a mixture model where every sequence is generated by a mixture of distributions associated to several clusters but does not need the data to be represented by a Gaussian mixture, which is the most commonly used representation of sequences and that gives significant modeling freedom. This algorithm also generates a very interpretable profile for the discovered Clusters,i.e, the latent groups of users who visited a food joint over a two year period. The Cluster profile, in this case, would contain the representative visit-counts of the cluster or group. The data that was used for the work has been contributed by a digital marketing company. The data is a collection of sparse sequences of a number of user visits to a food joint, where each entry of a user's sequence is the number of visits of that user per week to a given restaurant.
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.