  Predicting the answer to a product-related question is an emerging field of
research that recently attracted a lot of attention. Answering subjective and
opinion-based questions is most challenging due to the dependency on
customer-generated content. Previous works mostly focused on review-aware
answer prediction; however, these approaches fail for new or unpopular
products, having no (or only a few) reviews at hand. In this work, we propose a
novel and complementary approach for predicting the answer for such questions,
based on the answers for similar questions asked on similar products. We
measure the contextual similarity between products based on the answers they
provide for the same question. A mixture-of-expert framework is used to predict
the answer by aggregating the answers from contextually similar products.
Empirical results demonstrate that our model outperforms strong baselines on
some segments of questions, namely those that have roughly ten or more similar
resolved questions in the corpus. We additionally publish two large-scale
datasets used in this work, one is of similar product question pairs, and the
second is of product question-answer pairs.
