Question: What are the features of used to customize target user interaction? 
Evidence: We represent each instance of image, question, and user choice as a triplet consisting of image feature, question feature, and the label vector for the user's answer. In addition, collecting multiple choices from identical users enables us to represent any two instances by the same user as a pair of triplets, assuming source-target relation. With these pairs of triplets, we can train the system to predict a user's choice on a new image and a new question, given the same user's choice on the previous image and its associated question. User's choice $x_{ans_i}$ is represented as one-hot vector where the size of the vector is equal to the number of possible choices. We refer to the fused feature representation of this triplet consisting of image, question, and the user's choice as choice vector.
As discussed earlier, we attempt to reflect user's interest by asking questions that provide visual context. The foremost prerequisite for the interactive questions to perform that function is the possibility of various answers or interpretations. In other words, a question whose answer is so obvious that it can be answered in an identical way would not be valid as an interactive question. In order to make sure that each generated question allows for multiple possible answers, we internally utilize the VQA module. The question generated by the VQG module is passed on to VQA module, where the probability distribution $p_{ans}$ for all candidate answers $C$ is determined. If the most likely candidate $c_i=\max p_{ans}$ , where $c_i \in C$ , has a probability of being answer over a certain threshold $\alpha $ , then the question is considered to have a single obvious answer, and is thus considered ineligible. The next question generated by VQG is passed on to VQA to repeat the same process until the the following requirement is met:
In our experiments, we set $\alpha $ as 0.33. We also excluded the yes/no type of questions. Figure 4 illustrates an example of a question where the most likely answer had a probability distribution over the threshold (and is thus ineligible), and another question whose probability distribution over the candidate answers was more evenly distributed (and thus proceeds to narrative generation stage).
We represent each instance of image, question, and user choice as a triplet consisting of image feature, question feature, and the label vector for the user's answer. In addition, collecting multiple choices from identical users enables us to represent any two instances by the same user as a pair of triplets, assuming source-target relation. With these pairs of triplets, we can train the system to predict a user's choice on a new image and a new question, given the same user's choice on the previous image and its associated question.
As discussed earlier, we attempt to reflect user's interest by asking questions that provide visual context.
In other words, a question whose answer is so obvious that it can be answered in an identical way would not be valid as an interactive question. 
 If the most likely candidate $c_i=\max p_{ans}$ , where $c_i \in C$ , has a probability of being answer over a certain threshold $\alpha $ , then the question is considered to have a single obvious answer, and is thus considered ineligible. 
In our experiments, we set $\alpha $ as 0.33. We also excluded the yes/no type of questions. 
Answer: The answers are shown as follows:
* image feature
* question feature
* label vector for the user's answer