Beyond Exchangeability: The Chinese Voting Process
Many online communities present user-contributed responses such as reviews of products and answers to questions. User-provided helpfulness votes can highlight the most useful responses, but voting is a social process that can gain momentum based on the popularity of responses and the polarity of existing votes. We propose the Chinese Voting Process (CVP) which models the evolution of helpfulness votes as a self-reinforcing process dependent on position and presentation biases. We evaluate this model on Amazon product reviews and more than 80 StackExchange forums, measuring the intrinsic quality of individual responses and behavioral coefficients of different communities.
The paper suggests Chinese Voting Process (CVP) to model user response patterns in online community where users can write a response or vote helpfulness of existing responses. The response patterns are divided into two phase: 1) selection phase and 2) voting phase. In the selection phase, a user decides to write a new response or vote an existing response. And once the user decided to vote, the user decides to give a positive vote or negative vote in the voting phase. With Amazon review and StachExchange datasets, the model shows that each community reveals different response patterns characterised by trendiness and conformity. 
The exchangeability under a probabilistic model gives a great flexibility in building and inferencing, while preventing the representation power of a model. This paper well captures two different levels of non-exchangeability occurring in online voting.  Although the problem is interesting, the presentation of this paper makes hard to understand the proposed approach. Especially, the structure and notations hinder readers from understanding some key ideas.   Below I listed some suggestions and comments to improve the paper:  * a short description of the reinforcing mechanism between the CRP and ddCRP: ddCRP is an important concept of the proposed approach, which breaks the general exchangeability assumption. But It is quite hard to get an intuition about how this model works to compare with the CRP. A brief comparison between two processes would be good.  * intrinsic quality: In the generative process described in table2, each item has an unique intrinsic quality over time. However, in equation (1), it seems the model jointly learns different intrinsic qualities at a different time. I assume that only one intrinsic quality is estimated in the experiment. Please clarify the notation across the manuscript.  * Table 2 [generative process]: When table 2 is introduced in the main text, there is no explanation about the notations in this table, and it's impossible to understand the process. Maybe move the table after section 2.2? It would be better to combine table 2 and 4 since both tables are talking about the same thing.  * Table 5: There is no description of table 5 in the main text. The paragraph between line 208 and 218 seems a relevant part, but the description of each table should be included in the main text. Also the voting is a binary classification problem, so it might be more intuitive to interpret the result if it's written in terms of accuracy(?).  * Prediction: since the suggested model is fully generative, once the model learns parameters, it would be also possible to have a full prediction task which predict two phases together. Without this, the propose model seems just a combination of two independent models.