Many natural language questions are inherently subjective. They can not be answered properly if we do not know the personal preferences of the answerer. For example, "Do you like cats?" There is no "the only correct answer" to this question. To answer it, the model has to be able to capture the persona of the answerers. However, the users usually do not answer different questions with equal chance. Instead, while some are answered with a high frequency, others are hardly answered by anyone. To deal with this imbalanced sparsity in data, we first introduce a Siamese Network to capture the preferences patterns of the users. Then the model is ensembled with an additional dense layer to predict the answers of the users. Applying to an online dating dataset, our approach achieves a high accuracy of 78.7%.