Neural Information Processing Systems http://nips.cc/

The exchangeability under a probabilistic model gives a great flexibility in building and inferencing, while preventing the representation power of a model. 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.

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.

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.